Free MLOps: Machine Learning in Production Bay Area

Oct. 12th – 13th, 2022

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A Uniquely Interactive Experience

Why MLOps Production & Engineering?

Too few companies have effective AI leaders or an effective AI strategy.

An interactive virtual conference is one in which you meet attendees before the event, message speakers directly, discuss specific areas of your work and establish a stronger network. We’re working to help you with a clearer understanding of best practices, methodologies, and principles of effective ML.

The free virtual micro-summit includes:

Join this initiative to help push the ML community forward.

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BREAKOUT SESSIONS
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Chair

James Le

Data Advocate / Partnerships Lead, SuperbAI

Speakers

Amir Servi

Head of Product, Superwise

Talk: A Guide to Multi-Tenancy Architectures in ML
See Bio & Abstract
Raymond Reed

Customer Success Data Scientist, WhyLabs

Talk: Monitoring CV Systems: A Unique Solution to A Unique Problem
See Bio & Abstract
Nikita Demir

Founding Engineer, Galileo

Talk: Common ML Data Issues in Public Datasets
See Bio & Abstract
Yaoshiang Ho

Head of Product, Masterful AI

Talk: Practical Approaches to Computer Vision
See Bio & Abstract
Thaddeus Fortenberry

Senior Principal Computer Architect, Tenstorrent

Talk: The Exciting Evolution of MLOps
See Bio & Abstract
Rolando Garcia

PhD Candidate, UC Berkeley

Talk: Operationalizing Machine Learning: An Interview Study
See Bio & Abstract
Daniel Siryakov

Product Manager, Comet

Talk: Multiply Your Image Search & Testing Capabilities using Comet
See Bio & Abstract
Alex Kim

MLOps Solutions Engineer, Iterative

Talk: Machine Learning Experimentation with DVC and VS Code
See Bio & Abstract
Jimmy Whitaker

Chief Scientist of AI, Pachyderm

Talk: Rethinking ML Development – A Data-Centric Approach
See Bio & Abstract

Workshop Facilitators

Amber Roberts

Machine Learning Engineer, Arize AI

Workshop: Troubleshooting your ML Models in Production
See Bio & Abstract
Eddie Esquivel

Senior Solutions Architect, Tecton

Workshop: Building a Movie Recommendation System on Tecton with Snowflake
See Bio & Abstract
Gustaf Cavanaugh

Field Team, ContinualAI

Workshop: Beginner’s Guide to Operational ML with Python for Snowpark
See Bio & Abstract
more to be announced
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Harish Doddi

Founder & CEO, Datatron

Harish Doddi has 10+ years experience building AI infrastructure systems early on at places like LYFT, TWTR and SNAP Some notable work of him relates to building LYFT AI pricing engine from scratch which contributes to a significant revenue today. He has finished his graduation from Stanford University and undergrad from International Institute of Information Technology, Hyderabad, India. He got inspired from his previous experiences to start Datatron, an Enterprise grade MLOps and Model Governance platform which helps large enterprises to accelerate the time to production for models. The platform has already been proven in several fortune 100 companies


Victor Thu

VP of Customer Success & Ops, Datatron

Victor Thu is the VP of Customer Success and Operations at Datatron where he is overseeing multiple functions including marketing, business development, operations, and customer success.
In the last five years, Victor has been with machine learning startups and have experienced first-hand the challenges companies have with AI. Whether they are startups or enterprises, a common challenge everyone has is on how to get AI models running in production.
Victor has worked closely with some of the top enterprises including those in banking and financial services, top ranked international airports, retailers, manufacturers, and more.



Abstract:

In this talk, I would like to focus on some of the lessons learnt deploying MLOps solutions in Enterprise. MLOps is a relatively new area and most of the time people are not sure which direction to go, what are some common pitfalls, how can they avoid them and also best practices that can be adopted early on in the model life cycle. I would like to focus on this aspect of the conversation.

What You'll Learn:

Best MLOps practices for IT and Machine learning engineers

Talk: Best Practices Deploying MLOps Solutions in Enterprise

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Gilad Shaham

Director of Product Management, Iguazio

Gilad has over 15 years of experience in product management and a solid R&D background. He combines analytical skills and technical innovation with Data Science market experience. Gilad’s passion is to define a product vision and turn it into reality. As Director of Product Management at Iguazio, Gilad manages both the Enterprise MLOps Platform product as well as MLRun, Iguazio’s open-source MLOps orchestration framework.
Prior to joining Iguazio, Gilad managed several different products at NICE-Actimize, a leading vendor of financial crime prevention solutions, including coverage of machine-learning-based solutions, formation of a marketplace, and addressing customer needs across different domains.
Gilad holds a B.A in Computer Science, M.Sc. in Biomedical Engineering, and MBA from Tel-Aviv University

Abstract:

There are many challenges to operationalizing machine learning, but perhaps one of the most difficult is online feature engineering. Generating a new feature based on batch processing takes an enormous amount of work for ML teams, and those features must be used for the training stage as well as the inference layer. Feature engineering for real-time use cases is even more complex. Real-time pipelines require an extremely fast and low latency event-processing mechanism, that can run complex algorithms to calculate features in real time. With the growing business demand for real-time use cases such as fraud prediction, predictive maintenance and real-time recommendations, ML teams are feeling immense pressure to solve the operational challenges of real-time feature engineering for machine learning, in a simple and reproducible way. This is where online feature stores come in. An online feature store accelerates the development and deployment of real-time AI applications by automating feature engineering and providing a single pane of glass to build, share and manage features across the organization. This improves model accuracy, even when complex calculations and data transformation is involved, saving your team valuable time and providing seamless integration with training, serving and monitoring frameworks.

What You'll Learn:

In this talk, we’ll cover the challenges associated with online feature engineering across training and serving environments, how feature stores enable teams to collaborate on building, sharing and managing features across the organization, solutions that exist to enable you to build a real-time operational ML pipeline that can handle events arriving in ultra-high velocity and high volume, calculate and trigger an action in seconds, how to build your ML pipeline in a way that enables ingestion and analysis of real-time data on the fly and how to monitor your real-time AI applications in production to detect and mitigate drift, to make your method repeatable and resilient to changes in market conditions.

Talk: Building Real-Time ML Pipelines with A Feature Store

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Amit Paka

CPO & Co-founder, Fiddler AI

Amit is the co-founder and CPO of Fiddler, a Machine Learning Monitoring company that empowers companies to efficiently monitor and troubleshoot ML models with Explainable AI. Prior to founding Fiddler, Amit led the shopping apps product team at Samsung and founded Parable, the Creative Photo Network, now part of the Samsung family. He also led PayPal's consumer in-store mobile payments launching innovations like hardware beacon payments and has developed successful startup products, particularly in online advertising - paid search, contextual, ad exchange, and display advertising. Amit has passions for actualizing new concepts, building great teams, and pushing the envelope. He aims to leverage these skills to help define how AI can be fair, ethical, and responsible.

Abstract:

In this session, we will talk about why monitoring is critical to ML success. ML models can fail silently and lose their predictive power. This talk will discuss the key reasons models fail and hurt business performance: model drift, data integrity, outliers and bias. Once identified, operational issues are time consuming to fix. This talk will focus on how cutting-edge Explainable AI and model analytics can help find the root cause of an operational issue quickly. MLOps is iterative. This talk will also outline how model and cohort comparisons can help reduce time to market for new models.

What You'll Learn:

- How to measure model drift and how it can help identify model degradation, even without ground truth
- How to monitor for key model metrics
- How to root cause issues with Explainable AI and model analytics
- How to build high performance ML iteratively with model and cohort comparisons
- How bias and outlier detection can help in model monitoring success

Talk: Build High Performance MLOps With ML Monitoring and AI Explainability

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Daniel Jeffries

Chief Technology Evangelist, Pachyderm

Dan Jeffries is Chief Technology Evangelist at Pachyderm. He’s also an author, engineer, futurist, pro blogger and he’s given talks all over the world on AI and cryptographic platforms. He’s spent more than two decades in IT as a consultant and at open source pioneer Red Hat.
With more than 50K followers on Medium, his articles have held the number one writer's spot on Medium for Artificial Intelligence, Bitcoin, Cryptocurrency and Economics more than 25 times. His breakout AI tutorial series "Learning AI If You Suck at Math" along with his explosive pieces on cryptocurrency, "Why Everyone Missed the Most Important Invention of the Last 500 Years” and "Why Everyone Missed the Most Mind-Blowing Feature of Cryptocurrency,” are shared hundreds of times daily all over social media and been read by more than 5 million people worldwide.

Abstract:

Just a few years ago, every cutting-edge tech company, like Google, Lyft, Microsoft, and Amazon rolled their own AI/ML tech stack from scratch. Fast forward to today and we've got a Cambrian explosion of new companies building a massive array of software to democratize AI for the rest of us. But how do we make sense of it all? We created the AI Infrastructure Alliance to bring all those companies together. That’s because for AI apps to become as ubiquitous as the apps on your phone, you need a canonical stack for machine learning that makes it easier for non-tech companies to level up fast. We need a LAMP stack for AI/ML to truly unleash the power of machine learning for companies big and small.

What You'll Learn:

Attendees will learn how the MLOps space is evolving and how they can adopt best of breed technologies to create their canonical stack.

Talk: The Rapid Evolution of the Canonical Stack for Machine Learning

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Adam Gugliciello

Senior Solutions Engineer, Dataiku

Adam is a 20 year veteran of data and software systems integration. He's spent the last decade helping the world's largest firms integrate their data with Hadoop, and scale their business capabilities with application networks. Adam now leverages all his skills to help those self-same industry giants to industrialize AI and Analytics with Dataiku.

Abstract:

1. Key Concepts in MLOps as Dataiku sees it
2. What constitutes readiness for deployment
3. Considerations for model monitoring, deployment and management strategies
4. How Dataiku can streamline your MLOps processes

Workshop: MLOPs with Dataiku: Considerations For Model Deployment & Monitoring

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Eric Duffy

Director, Tenstorrent

Eric is a business development director at Tenstorrent, a company designing microprocessors tailored for Machine Learning training and inference workloads from the edge to the data-center. Eric's experience in the AI domain spans 15 years, having developed Computer Vision applications for life-sciences, consulted on AI with the United Nations technology division, ITU, and having worked with a large FinTech on next-generation AI-enabled transaction banking services.

Abstract:

Machine Learning is driving up compute demand, but so much of that activity is wasted cycles. Join Tenstorrent as we explore what is driving up the volume of computation, why throwing money at GPUs isn't the solution, and how we need to be smarter about what we process.

Talk: Next-Gen AI Processors, and Why We Need to Think Differently About SW 2.0

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Matt Zeiler

Founder & CEO, Clarifai

Matthew Zeiler, founder and CEO of Clarifai, is a machine learning Ph.D. and thought leader pioneering the field of applied artificial intelligence. Matt has worked alongside renowned machine learning pioneers Geoff Hinton, Rob Fergus, Yann LeCun and Jeff Dean. This experience cultivated his passion for convolutional and deconvolutional neural networks and visual recognition. Matt's unprecedented knowledge and know-how in deep learning landed him winning the top five places in image classification at the ImageNet 2013 competition. Matt received his undergraduate degree at University of Toronto and a Ph.D. in machine learning and image recognition from New York University.

Abstract:

The quality of training, validation and test data have huge impacts on model performance. Companies with unlimited budgets can theoretically train models on millions of perfectly labeled inputs and process these images with unlimited compute power. But the reality for most companies is that there are serious time and resource constraints when trying to get AI into production. If you’re like most organizations, you’re labeling the work internally and looking for ways to reclaim your teams’ time to focus on higher-value initiatives.
Learn how to accelerate the process of building AI-powered applications and speed time-to-value 100x. See how using one dedicated AI platform that includes data labeling and pre-built AI models will save your organization time and improve the quality of your training data. Models can be pushed to production quickly and then you can iterate on them to perfect your designs over time. This session explores two separate approaches to training detection models: Region Classification Workflows and Deep-Trained Object Detectors. See how to power data labeling pipelines that accelerate the process of training specialized models.

What You'll Learn:

How to jump start your project with “zero-shot” learning
How to quickly iterate on your designs using only a few data samples and “quick training”
How to develop a baseline for model performance
How to use AI-automation to speed model development by 100X

Talk: Deploying Efficient Data-Centric AI 100x Faster

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Eric Schles

Data Scientist, Johns Hopkins University

Eric Schles is a principal data scientist at Johns Hopkins University. He is also a PhD student at CUNY Graduate Center, studying machine learning and category theory.

Abstract:

Eric Schles talks about Drifter-ML, which is a testing framework that helps ensure your machine learning models satisfy the assumptions you made when training your model. He’ll walk through the motivations of modeling testing and a few examples of how to use the framework.

What You'll Learn:

The idea behind drifter-ml and how to use it.

Talk: Introduction to Drifter-ML

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Christopher A. Choquette Choo

Machine Learning Researcher, Google Brain

Christopher is an AI Resident (2020) at Google researching in the areas of model interpretability, federated learning, and differential privacy. Previously, Christopher worked on adversarial machine learning research in the CleverHans Lab at the Vector Institute. He explored privacy attacks and defenses for machine learning, confidential and private collaborative learning protocols, and protecting machine learning from IP theft.
Christopher also has experience in production ML settings. He created DualDNN while at Intel Corp., designed an open source AutoML while at Georgian Partners LP, and contributed to the open source Differential Privacy package Tensorflow/Privacy.

Abstract:

Production machine learning models and application programming interfaces can be accessed by anyone, including malicious users or `adversaries'. Adversaries can exploit weaknesses in machine learning models to breach the privacy of the user training data or of the trained machine learning model itself, which is considered valuable intellectual property. These concerns become exacerbated when owners of data or models wish to collaborate, so as to collectively improve their machine learning algorithms. In this talk, we'll go over state of the art research at the intersection of privacy and machine learning, in particular, we'll explore how to estimate privacy leakage, defend against it, and how to collaborate without compromising privacy. We'll also go over a new research area enabling Machine Unlearning, so as to comply with new legislation like GDPR that gives more power to the users to withdraw and remove their data, potentially even from already trained machine learning models.

What You'll Learn:

The SOTA in privacy attacks and defenses, in particular of the canonical Membership Inference attack.
How to verify ownership of a machine learning model and defend against model extraction (stealing) attacks.
How to collaborate without compromising privacy.
Efficient techniques to enable guaranteed unlearning of user data.

Talk: The Privacy Considerations of Production Machine Learning

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Milecia Mcgregor

Developer Advocate, Iterative.ai

Milecia is a senior software engineer, international tech speaker, and mad scientist that works with hardware and software. She will try to make anything with JavaScript first. In her free time, she enjoys learning random things, like how to ride a unicycle, and playing with her dog.

Abstract:

It's easy to lose track of which changes gave you the best result when you start exploring multiple model architectures. Tracking the changes in your hyperparameter values, along with code and data changes, will help you build a more efficient model by giving you an exact reproduction of the conditions that made the model better.
In this talk, you will learn how you can use the open-source tool, DVC, to increase reproducibility for two methods of tuning hyperparameters: grid search and random search. We'll go through a live demo of setting up and running grid search and random search experiments. By the end of the talk, you'll know how to add reproducibility to your existing projects.

What You'll Learn:

How to perform grid search and random search for hyperparameters
How to compare metrics across different experiments to get the best model

Talk: Using Reproducible Experiments To Create Better Machine Learning Models

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Mike Purewal

Front Office Data Scientist, Bank of America

Mike Purewal is a Front office data scientist and product leader with expertise driving business value utilizing technology, programming and machine learning. He has spent more than a decade in financial services and is currently a Director in Global Equities at Bank of America. Mike is also an Adjunct Professor at NYU where he teaches a Machine Learning in Finance course with an emphasis on practical implementation. He received his PhD in Applied Physics from Columbia University where he pioneered cutting edge research on electron transport in low dimensional material, receiving more than 2k citations.

Abstract:

Trust is a factor in gaining adoption of ML projects. Having strong model interpretability practice enforces trust, results in productive dialogue and closes the mutual subject matter gap between the end-user and data scientist, all driving better adoption rates. Model interpretability should reflect that ML models are often conceptually a codified, scaled version of heuristics already employed by the business. Once established, these practices form a backbone for all stakeholders, including the monitoring and maintenance tools used by MLOps. This talk addresses techniques to achieve this quantitatively, technically and organizationally.

What You'll Learn:

Techniques to achieve model adoption: quantitatively, technically and organizationally.

Talk: 5 Techniques to Increase Adoption Rates via Interpretability

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Samantha Zeitlin

Security, Principal Machine Learning Engineer, Elastic

Samantha Zeitlin is a former cancer researcher with a PhD in biochemistry, where she specialized in DNA damage, cell division, and high-throughput image analysis (aka "big data"). She has been working in software for about ten years, including stints at multiple startups, Yahoo, and as an independent consultant.

Abstract:

The scenario: you’ve just started at a new job, and the crown jewel machine learning model is becomes stale at a consistent pace, so it has to be re-trained and re-released sooner rather than later. The real problem is that training and deploying a model takes 2 weeks, and it’s done almost entirely by hand. And the last person who was doing this just left. How do you go about starting to automate this? Oh and actually you’re supporting two products: the new one, and the legacy one from the startup that was acquired by the bigger company that you just joined. Which means you now have to learn and automate two completely different processes for shipping the same model, but the documentation is sparse and contains a lot of ambiguous terminology. The good news is, this is a highly collaborative environment, and everyone on the team has been working remotely since before the pandemic started. In this talk, I’ll share the story of how we rewrote the code to get 7 different artifacts deployed, and some of the tradeoffs we had to make to get it done in less than a month. I’ll talk about some things we learned the hard way, some things I wish I had done differently, and how we’re planning to make this all easier.

What You'll Learn:

1) What not to do with model version tracking
2) What kinds of artifact tests and validation can be automated, and how
3) Some challenges of migrating from legacy scripts to productionized code

Talk: Deploying Model Artifacts in Hard Mode

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Mengdi Huang

Deep Learning Engineer, NVIDIA

Mengdi Huang is a deep learning engineer at NVIDIA with six years of experience working in various DL-based AI research and application areas, including MLOps, recommender systems, and multimodal language, vision, and speech processing.


Shashank Verma

Technical Marketing Engineer, NVIDIA

Shashank Verma is a Deep Learning Technical Marketing Engineer at NVIDIA. He is responsible for developing and presenting developer-focused content on various Deep Learning frameworks and libraries. He obtained his master's in Electrical Engineering from the University of Wisconsin-Madison, where he focused on Computer Vision, security aspects in Data Science, and High Performance Computing.



Abstract:

Recommendation systems must constantly evolve through the digestion of new data and algorithmic improvements of the model for its recommendations to stay relevant and effective in production. In this talk, we focus on leveraging MLOps tools and practices to operationalize a recommendation system at scale and continuously deliver improvements in production. To demonstrate this, we use NVIDIA Merlin, an open-source framework for building GPU accelerlearning recommender systems, along with Kubeflow as an orchestrator on Google Kubernetes Engine.

What You'll Learn:

- Continuous training, serving, monitoring, and autoscaling of AI systems
- How to build large scale GPU accelerated recommender systems

Talk: Continuously Improving Large Scale Recommenders with MLOps Tools and Practices

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Jaya Kawale

Director of Machine Learning, Tubi

Jaya Kawale is the Director of Machine Learning at Tubi and her team works on solving various ML problems for the product ranging from recommendations, content understanding and acquisition and ads ML. Prior to that, she worked at Netflix as a lead research scientist. She has also worked at Adobe research and Yahoo research in the past. She did her PhD in Computer Science from the University of Minnesota working on graph based approaches to understand climate data. She has published several papers at various conferences like NeurIPS, KDD, IJCAI, SDM, etc.

Abstract:

Machine Learning is widely used to solve a myriad of problems for video on demand streaming services ranging for homepage personalization to understanding content for acquisition and even ads targeting. In this talk, I will focus on some of the challenges faced in the real-work environment such as balancing various business metrics, feedback loops in recommendations, model interpretability and scalability, algorithmic fairness and biases. I will also talk about some of our solutions to the problems along with some future work.

What You'll Learn:

Industrial application of machine learning to solve challenging problems.

Talk: Machine Learning for Entertainment: Challenges and Opportunities

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Ken Conroy

VP of Data Science, Finn AI

Dr. Ken leads the development of the Finn AI proprietary Natural Language Processing system. He is an expert data scientist and a sought-after speaker at technical events locally and abroad. Dr. Ken earned his PhD for Computer Science at Dublin City University. His specialties include deep machine learning and artificial intelligence.

Abstract:

Constructing and maintaining an ever evolving supervised learning approach to conversational assistants is a complex task. This talk will discuss the approach required to anticipate and adapt to changing needs. How much of your data model must be pre-planned, what tooling is required to maintain and alter your labeled data and annotation requirements? How do you visualize and identify where gaps remain in your models capabilities, and how can you ensure quality does not suffer from increased coverage. This talk will speak about how this approach was applied over the last few years, leading to the development of our conversational assistant at Finn AI.

What You'll Learn:

Focus on your data, improve your data management capabilities to truly understand and solve your customer needs.

Talk: How A Data-Centric Approach to ML led Finn to World-Leading Conversational AI for Banking

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Vered Shwartz

Assistant Professor, UBC Computer Science

Vered Shwartz is an Assistant Professor of Computer Science at the University of British Columbia. Her research on natural language processing focuses on commonsense reasoning, computational semantics and pragmatics, and multiword expressions. Vered was a postdoctoral researcher at the Allen Institute for AI (AI2) and the University of Washington, and received her PhD in Computer Science from Bar-Ilan University. Vered's work has been recognized with several awards, including The Eric and Wendy Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences, the Clore Foundation Scholarship, and an ACL 2016 outstanding paper award.

Abstract:

Deep Learning has changed the face of Natural Language Processing (NLP). Across NLP tasks, generic neural architectures surpass the performance of systems designed based on domain knowledge, and in some cases even perform on par with humans. The recent advancement of pre-trained language models (such as Google's BERT), trained on a massive amount of texts, further boosted performance and reduced development time. Their ease of use has made NLP accessible to non-experts. But looking beyond popular media reports and performance metrics, is NLP anywhere near being solved? In this talk I will present some of the remaining challenges in NLP. I will discuss current blind spots that limit the real-world applicability of models, such as their limited ability to generalize outside the training domain, the vast data requirements, and the lack of common sense knowledge. We will also review broader consequences related to environmental and ethical issues.

What You'll Learn:

The talk is built as a checklist of what works and what doesn't yet work.

Talk: Recent Breakthroughs and Uphill Battles in Modern Natural Language

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Toren Huntley

Senior Data Scientist, Atreides

Toren is the lead data scientist of Atreides, a startup providing leaders from defense, intelligence, and humanitarian organizations with the online backbone for making high stakes, centralized and strategic decisions. Atreides brings global enriched datasets to its users at lightning speed, spanning land, sea, air, mobile and cyber domains. Toren leads data science with a customer-focused mindset to turn the science of complexity into the art of simplicity. She uses a first-principles approach to ensure data solutions are valid, explainable, and lasting.

Abstract:

The most successful ML solutions start with desired outcomes and the right data. We’ve all heard the statistic that 80% of the effort of an ML project is spent on data cleaning. Not only is this true, but it only increases when moving a model into production. Having good data practices will pay off in saved time and effort, especially when the full team is on board. There are three main questions to ask when creating an end-to-end ML solution.

What You'll Learn:

Understanding the entire data process is critical for accurate and long-lasting ML models.

Talk: A Data First Approach

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Amir Abdi

Senior Research Engineer / Research Team Lead, Borealis AI

Amir received his PhD in Computer Engineering from the University of British Columbia with research topics in computer vision and reinforcement learning, and focusing on biomedical solutions. He is a recipient of the NSERC Vanier Scholarship and Gilles Brassard Award for his interdisciplinary research. Amir is the CTO and co-founder of Offerland, a proptech AI-based startup in Vancouver. He is currently a Senior Research Engineer in Borealis AI and leads the NOMI Forecasts team.

Abstract:

AI systems are biased because their models and data are human creations and reflect the inherent racial, economic, and gender inequalities in the society. Bias can enter the collection, generation, and labeling of data as well as the design and evaluation of algorithms. This, often unintentional, phenomenon adversely impacts predictions and poses a danger for the business and its clients. The concept of bias in AI, to some extent, is tied to the ethics and fairness concepts as it can result in discriminatory outcomes for certain minorities depending on the applications.
In this talk, the main roots of bias in AI systems and their impact on the business and the society are discussed. Some of the essential technical solutions for model explainability and model debiasing, including data-centric and algorithmic approaches, as well as model monitoring solutions, are explored. Finally, the executive actions organizations could exercise to raise awareness and mitigate the bias risk are laid out.

What You'll Learn:

Importance of fairness in ML systems and how to monitor it.

Talk: Monitoring AI Fairness

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Razi Bayati

Vancouver Chapter Chair, MLOps World: Machine Learning in Production

Razi Bayati is a UBC alumni and currently works at Rogers communications as a machine learning engineer. Her area of research is the intersection of machine learning and 5G.
Before joining Rogers, she worked in enterprise-level companies - Huawei and Nokia, successful startups - Theory + Practice and Zennea Technologies, and the telecommunication lab at the University of British Columbia.
She has always tried to bring the community together, previously as an elected vice president of academic and university affairs at the graduate student society of UBC to advocate for all graduate students and currently as a co-chair of women in technology group with RISE for Women.
As chair, Razi is determined to bring together the Vancouver machine learning society with her vision to promote data-centric AI. Her goal is to connect BC-based companies and improve their visibility to expose the potentials in BC for machine learning in production.

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Zaid Haddad

Data Scientist Leader, Slalom

Zaid is a Data Scientist Leader at Slalom. He brings experience delivering high quality data science solutions in personalized healthcare and big data while supporting commercialization in regulated environments. He has supported several organizations in the development and implementation of data science products’ road map from conception to deployment. Zaid holds his Bachelor of Science from Simon Fraser University in Computing Science and Molecular Biology & Biochemistry.

Abstract:

Meet the MLOps challenge with Slalom to unleash the power of intelligent applications.

What You'll Learn:

Build ML models at scale and MLOps framework

TalK: Machine Learning Operations

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Mohammad Ghodratigohar

Data Scientist and AI Cloud Solution Architect,Microsoft

Mohammad Ghodratigohar (MG) is a Microsoft data scientist and cloud solution architect with years of hands-on knowledge and expertise in leveraging advanced AI techniques to solve business problems. He joins Microsoft from Transport Canada, where, in collaboration with Microsoft’s account team, he played a key role in developing Transport Canada’s big data & MLOps platform on Microsoft Azure. Also, he designed, developed, and led various machine learning projects mainly in healthcare through private sector companies and hospitals in computer vision, signal processing and geospatial analysis fields. Mohammad has a MSc degree in biomedical engineering with a specialization and journal publications in machine learning and healthcare.

Abstract:

Throughout the development and use of machine learning models, the end-to-end MlOps process should addresses specific risks of AI with a focus on risk-adjusted value delivery and efficiency in AI scaling in alignment with regulations.
Responsible machine learning contains values and principles which at the core encompasses understanding explainability and fairness of model. This is critical for data scientists, auditors, and business decision-makers alike to ensure compliance with company policies, industry standards, and government regulations in their MLOps process.
This session will talk about how, where and when responsible AI principles can be applied in different MlOps steps through showcasing hands-on demo that uses the open-source techniques for assessing explainability and fairness of trained model.

What You'll Learn:

What is Responsible AI in action
How each component of Responsible AI can be applied and considered in MLOps
How some of Responsible AI toolkits fit into MLOps process
Actual example of applying Responsible AI toolkits in Azure Machine learning

Talk: Responsible MLOps

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Greg Loughnane

Lead Instructor, MLOps, FourthBrain / Product Manager, Career Coach, FactoryFix

Greg is the lead instructor of the MLOps program at FourthBrain, where he teaches a cohort-based course that aims to give ML practitioners the skills necessary to deploy, scale, and monitor ML models in production environments.
He also serves as product manager for Career Coach at FactoryFix, which aims to help skilled manufacturing professionals level-up their careers through personalized and proactive guidance.
A former founder, data science consultant, professor, and researcher, Greg's current interests lie in maximizing value through community building and AI-first product thinking.

Abstract:

The most valuable tech companies in the world have successfully leveraged network effects and data to create unparalleled business value and competitive advantage. Yet today, it still remains difficult to replicate their success. Why? While being able to operationalize ML in production environments is incredibly important, other barriers to value creation remain. Integrating ML into existing digital product paradigms requires a holistic approach to AI-first product management, both from technical and business perspectives. In this talk, a framework for AI-first product management is presented that considers product, infrastructure, process, and people problems in turn. These problems correspond to answerable questions about the why, what, how, and who of MLOps. Best practices for digital product portfolios and pipelines are discussed through the lens of maximizing the business value of MLOps for any organization in 2022.

What You'll Learn:

Actionable recommendations about MLOps, especially about what not to do. Maybe even some guidance or additional confidence about where to allocate limited resources to maximize value from MLOps initiatives.

Talk: Business-Value Best Practices for MLOps: Building Data Science into Digital Products

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Stella Wu

Head of Machine Learning, Shakudo

Stella is a machine learning researcher experienced in developing AI models for real life applications. Stella has built machine learning models in natural language processing, time-series prediction, self-supervised learning, recommendation system and image processing at BMO, Borealis AI and several startups. Stella has a PhD in geophysical modeling from University of Münster in Germany.

Abstract:

AutoML is a capability that can scale up AI adoption across industries. MLOps tools can enable AutoML end to end to make it easy to get started and use in production.

What You'll Learn:

AutoML and MLOps are the The next frontier in large scale adoption of AI

Talk: AutoML and MLOps

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Yaser Khalighi

Founder and CEO, SceneBox

Yaser Khalighi (Stanford Ph.D.) is a leader on the frontier of machine learning data operations for computer vision and autonomous systems. Having previous experience building production-grade AI data platforms for autonomous vehicles and other applications, Yaser has spent the last two years at the helm of Caliber Data Labs. Caliber is the creator of SceneBox, the premier data operations platform for all things computer vision. Prior to his work building AI data platforms, Dr. Khalighi was a Senior Manager of Data Analytics at Ericsson.

Abstract:

It's all in the data.

As machine learning (ML) development continues at a frenetic pace across the mobility sector, many teams are neglecting the required parallel development of intelligent data operations solutions. In order to wrangle the vast amounts of unstructured perception data they collect, teams need to have access to a purpose-built, production grade data operations platform to expedite their ML development. In this talk, our speaker argues that optimized data operations in and of itself is the key to unlocking L4 and L5 autonomous systems. Even teams with seemingly sufficient tools may find value in exploring this ever-changing landscape.

What You'll Learn:

The importance of data operations when developing autonomous systems.

Talk: Software 2.0 and Data-Centric AI

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Sonya David

Strategic Analytics Solutions Manager, JPMorgan Chase & Co.

Sonya David is a Strategic Analytics Solutions Manager at JPMorgan Chase & Co. She has experience in data science and analytics. Sonya holds an Honours Bachelor’s degree in Neuroscience from the University of Toronto and a Master of Computer Science from Wilfrid Laurier University.
Sonya is deeply involved in the community, having volunteered for institutions such as Sick Kids and Sunnybrook, among others, holding positions ranging from Research Assistant to Committee Executive.
As Chair, Sonya will bring her committee experience, data science chops, and wide-ranging partnerships to the role.

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Jacopo Tagliabue

Director of AI, Coveo

Educated in several acronyms across the globe (UNISR, SFI, MIT), Jacopo Tagliabue was co-founder of Tooso, an A.I. company acquired by Coveo in 2019. Jacopo is currently the Director of A.I. at Coveo, shipping models to hundreds of customers and millions of users. When not busy building products, he teaches MLSys at NYU and explores topics at the intersection of language, reasoning and learning (with research work presented at NAACL, RecSys, ACL, SIGIR). In previous lives, he managed to get a Ph.D., do sciency things for a pro basketball team, and simulate a pre-Columbian civilization.

Abstract:

As with most Machine Learning systems, recommender systems are typically evaluated through performance metrics computed over held-out data points. However, real-world behavior is undoubtedly nuanced: ad hoc error analysis and case-specific tests must be employed to ensure the desired quality in actual deployments. We introduce RecList, a behavioral-based testing methodology and open source package for RecSys, designed to scale up testing through sensible defaults, extensible abstractions and wrappers for popular datasets.

What You'll Learn:

Recommender systems are everywhere, but it's hard to make sure they do what they are supposed to when actually deployed. We motivate our use case leveraging real-world experience across hundreds of organizations, and then discuss the shortcomings of standard research metrics. Finally, we introduce RecList, an open source tool that helps scale up behavioral testing for RecSys.

Talk: Beyond NDCG: Behavioral Testing of Recommender Systems with RecList

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David de la Iglesia Castro

Software Engineer, iterative.ai

David is a software engineer at iterative.ai, where he mainly works on open source tools like DVC and DVCLive. Before that, he was a Senior Computer Vision Researcher at gradiant.org.

Abstract:

Machine learning operations (MLOps) have gained attention among practitioners aiming to automate the development of Machine Learning models, attempting to mimic the impact of DevOps in software.
However, MLOps platforms are usually built isolated from the software development process, arguing that the well-proven tools used for DevOps can’t be applied to Machine Learning projects.
In this workshop, we will use HuggingFace to train a model that predicts labels for GitHub issues.
By extending the power of Git and Github with DVC and CML, our workflow will be able to handle the entire lifecycle of a Machine Learning model using the same tools and platforms that have been proven to work for software development.
The workshop only requires a web browser in order to follow from start to finish.

What You'll Learn:

In this workshop, we will learn what it means and how to build an "MLOps workflow" by extending the power of Git and GitHub with open-source tools. At the end of the workshop, we will have a workflow that covers the entire lifecycle of a Machine Learning mode

Workshop: Making MLOps Uncool Again

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Jason Katz

ML Engineer, LinkedIn

Jason Katz, a leading ML engineer at LinkedIn's job search ML team, and a top ranking freelancer on Upwork, teams up with his graduate school friend David Kebudi, the Co-Head of AI at OpenRisk Technologies with pending patents in Imitation Learning and Natural Language Processing. Jason and David got their Master's in Data Science from Brown University in 2020, after working on DARPA funded projects and cancer research, respectively.

Abstract:

Have you ever wondered how machine learning differs across various domains? Training models on petabytes of data that are then deployed to millions of users is very different from trying to create a predictive model from only a few data points that need to be deployed into a client's pipeline. Properly maintaining pipelines that generate in house data versus scouring the web for supplemental data versus working with clean datasets to test theories, the process couldn't be more varied! Join us to explore the differences between the ML experience at all levels!

What You'll Learn:

You will have an opportunity to hear about 3 main walks of ML life:
(1) Start-up ML, which often has limited data, and needs to prove itself to be useful within the strict limitations to secure value and funding;
(2) Big-Tech ML, with a vast ocean of data and resources, these projects often have effects on millions of people - which requires extensive testing before deployment and iterative problem solving strategies for improvement;
(3) Academia, which requires innovative thinking and experimenting while keeping the budget under control.
The talk aims to give exposure to the differences among ML teams across the industry, hopefully helping people make easier decisions about their career.

Talk: ML Everywhere - Start Up, Big Tech, Academia

Co-Presenter: David Kebudi

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David Kebudi

ML Engineer, OpenRisk Technologies

David Kebudi, the Co-Head of AI at OpenRisk Technologies with pending patents in Imitation Learning and Natural Language Processing, teams up with his graduate school friend Jason Katz, a leading ML engineer at LinkedIn's job search ML team, and a top ranking freelancer on Upwork. David and Jason got their Master's in Data Science from Brown University in 2020, after working on DARPA funded projects and cancer research, respectively.

Abstract:

Have you ever wondered how machine learning differs across various domains? Training models on petabytes of data that are then deployed to millions of users is very different from trying to create a predictive model from only a few data points that need to be deployed into a client's pipeline. Properly maintaining pipelines that generate in house data versus scouring the web for supplemental data versus working with clean datasets to test theories, the process couldn't be more varied! Join us to explore the differences between the ML experience at all levels!

What You'll Learn:

You will have an opportunity to hear about 3 main walks of ML life:
(1) Start-up ML, which often has limited data, and needs to prove itself to be useful within the strict limitations to secure value and funding;
(2) Big-Tech ML, with a vast ocean of data and resources, these projects often have effects on millions of people - which requires extensive testing before deployment and iterative problem solving strategies for improvement;
(3) Academia, which requires innovative thinking and experimenting while keeping the budget under control.
The talk aims to give exposure to the differences among ML teams across the industry, hopefully helping people make easier decisions about their career.

Talk: ML Everywhere - Start Up, Big Tech, Academia

Co-Presenter: Jason Katz

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Kenneth Chen

Former Machine Learning Engineer, ArthurAI

Kenny was a former MLE @ ArthurAI, creating enterprise software for monitoring production models for performance, data drift, bias mitigation, and explainability, where he researched the content of this presentation and productionized it in Arthur's B2B SaaS Platform.
He holds an undergraduate degree from Harvard University in Statistics and Computer Science, with a focus in Bayesian deep learning.

Abstract:

Automating data drift thresholding is a must for ML monitoring platforms for detecting model degradation as a result of shifting model inputs. There are many different data drift measures and many different distributional forms for reference data and observed data that raise various robustness concerns. To fix and address these concerns, we present two solutions, an empirical bootstrapping-based approach and flexible closed-form probabilistic approximations based on an adjusted Bayesian conjugacy, for f-divergence data drift thresholding. Robustness and computational trade-offs will be addressed.

What You'll Learn:

(1) Specific algorithmic approaches for automating data drift thresholds based on empirical approximations of data distributions, via bootstrapping and closed-form probability derivations/series approximations.
(2) Pitfalls to watch for (skewed data, low data sizes/high number of categories leading to sampling instability and degraded data drift thresholds) and how the proposed approaches are robust in these scenarios.
(3) Performance and computational trade-offs between the proposed approaches and summary of which to use depending on backend infrastructure.

Talk: Automating Data Drift Thresholding and Addressing Common Computational and Theoretical Pitfalls in Production ML Monitoring

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Mohamed Sabri

Senior Consultant in MLOps, Rocket Science

Mohamed Sabri is a results-driven Machine learning Engineer and MLOps specialist with 8 years of experience in Machine learning and data field. This Trilingual consultant has a very wide range of project experience for various industries. Mohamed Sabri has been as well a director of data science and AI in a startup in Montréal, with his experience on the field and as a manager Mohamed is capable of supporting all organization in their AI project implementation. Mohamed is also the author of the book “Data Scientist Pocket Guide” and instructor and mentor at the University of Austin Texas.

Abstract:

Description: The workshop is a hands-on session where we will discover Kubeflow pipelines. We will learn how to create an environment with Kubeflow on Kubernetes then get familiar with the environment. After that we will create our pipelines and upload them in Kubeflow. We will create couple of pipelines and run schedules for retraining.
Session length: 4 to 5 hours.
Requirements: Have a personal AWS account with credit card processed
Background: Knowledge in Docker/Kubernetes/Virtualization
Experience with Python

What You'll Learn:

Become more comfortable with tools like Kubeflow

Hands on Workshop: How to Build Pipelines with Kubeflow

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Alessya Visnjic

CEO, WhyLabs

Alessya Visnjic is the CEO of WhyLabs, the AI Observability company building the interface between AI & human operators. Prior to WhyLabs, Alessya was a CTO-in-residence at the Allen Institute for AI, where she evaluated commercial potential for the latest AI research. Earlier, Alessya spent 9 years at Amazon leading ML adoption & tooling efforts. Alessya is also the founder of Rsqrd AI, a global community of 1,000+ AI practitioners who are making AI technology Robust & Responsible.

Abstract:

The day the ML application is deployed to production and begins facing the real world is the best and the worst day in the life of the model builder. Debugging, troubleshooting & monitoring takes over the majority of their day, leaving little time for model building. In DevOps, software operations are taken to a level of an art. Sophisticated tools enable engineers to quickly identify and resolve issues, continuously improving software robustness. In the ML world, operations are still largely a manual process that involves Jupyter notebooks and shell scripts. One of the cornerstones of the DevOps toolchain is logging, it’s the foundation of testing and monitoring tools. What does logging look like in an ML system?
In this talk we will show you how to enable data logging for an AI application. We will discuss how something so simple enables testing, monitoring and debugging of the entire AI pipeline. We will dive deeper into key properties of a logging library that can handle TBs of data, run with a constraint memory footprint and produce statistically accurate log profiles of structured and unstructured data. Attendees will leave the talk equipped with best practices to supercharge MLOps in their team.

What You'll Learn:

The audience will leave the talk equipped with a set of best practices around logging, monitoring, and observability which they can implement in their organization. Best practices will focus on process, culture, and open source tools - so will be actionable for an organization of any size and stage of ML adoption.

Talk: The Critical Missing Component in the Production ML Stack

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Yuval Fernbach

CTO & Co-Founder, Qwak

Yuval Fernbach is the Co-founder & CTO of Qwak, where he is focused on building next-generation ML Infrastructure for ML teams of various sizes. Before Qwak, Yuval was an ML Specialist at AWS , where he helped AWS Customers across EMEA with their ML challenges. Previous to that, he was the CTO of the IT department of the IDF ("Mamram").

Abstract:

As more and more companies are building ML based production systems, practitioners are finding out that maintaining and managing models in production is difficult and expensive.
Building a production grade model contains many “moving parts”; Data / Code changes, package dependencies, research/production infrastructure, production interface, etc.

What You'll Learn:

In this session, we’ll cover some of the best practices of building models that can scale, fit production needs, and ready for the real world

Talk: Why Build System is Probably the Most Critical Thing When Shifting ML From Buzz to the Real World

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Raluca Crisan

Co-Founder, Etiq AI

Raluca has 10+ years of experience in data science working with a variety of clients (UK retailers, banks & telco companies). Raluca's experience spans managing teams to hands-on data product development. She is Co-founder of Etiq, a start-up in the ML observability space, with a focus on the algorithmic bias problem. Prior to Etiq she was Director - Data Science for Merkle Aquila.

Abstract:

There is a good deal of research creating metrics to help industry practitioners tackle the algorithmic bias problem. However, in practice, if you manage to reduce one bias metric you're likely to increase another. This talk will focus on setting forth strategies and examples of how practitioners can still use these bias metrics to obtain meaningful results. Technical implementation details will be discussed.

What You'll Learn:

- What are the key bias indicators/metrics? And how can they each help you? What are some strategies to deal with conflicting results...
- Technical implementation will be discussed for some metrics, including pointers on what are some things you want to be mindful of when you choose one calculation over another.
- Intersectionality is not covered as much in the academic literature as other bias topics, but it is a key topic in an applied setting. What are some strategies there?
Examples will help you understand how this can relate to your day-to-day work as data scientist/ML practitioner.

Talk: How Do You Identify Bias in a World of Conflicting Metrics?

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Gift Ojeabulu

Data Scientist, CBB Analytics

A Data Scientist, Machine Learning Engineer, Open Source Advocate, Occasional Speaker and Writer, Founder of The African Data Community Newsletter and a Community Advocate who is passionate about democratizing data knowledge to everyone and constantly providing guidance to enthusiast on how to navigate and transition in the data science space with experience in community management, social media content creation and Developer Education.

Abstract:

Deepchecks is a minimally intrusive MLOps solution for continuous validation of machine learning systems, meant to enable you to trust your models through the continuous changes in your data lifecycle. Deepchecks includes the must-have features for any ML Monitoring system: performance monitoring, data drift detection and anomaly detection alerts, along with some unique features that are especially helpful for complex ML pipelines: Monitoring various phases of the pipeline, detecting hidden data integrity issues, detecting low confidence segments, detecting inconsistencies that are hidden within unstructured text, etc. ML output are difficult to validate as manual testing only work for small dataset, Deepchecks is built for Machine learning engineers and data science leaders for continuous validation of machine learning systems as this is a sure way to scale.

What You'll Learn:

- What is Deepchecks
- When to use Deepchecks
- Data Centric and Model Centric with Deepchecks

Talk: Validating Your Machine Learning Models and Data With Minimal Effort Using DeepChecks

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Itay Ben Haim

ML Engineer, Superwise

Itay is an ML engineer with a background in software and data. 33, married, father of 1 baby girl, 1 dog, and 1 cat, who all get along with each other. In his free time (not that dads have much of that), he's into Crossfit and surfing.

Abstract:

In this workshop, we'll take a dive into MLOps CI/CD + CT pipeline automation. Part 1, we’ll focus on how to put together a continuous ML pipeline to train, deploy, monitor, and retrain your models. Part 2 will focus on automations and production-first insights to detect and resolve issues faster.
This hand-on build will be done with the Google Vertex platform, Superwise model observability, and retraining notebooks. Ahead of the event you’ll get access to all the platforms, environments, and resources we’ll use so you can follow along during the session.

What You'll Learn:

- How to build a continuous MLOps stack - Platform and tool alternatives for each step
- Considerations for scaling up
- Production-first insights and automations

Workshop: A Guide to Putting Together a Continuous ML Stack

Co-Presenter: Or Itzary

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Or Itzary

Chief Architect, Superwise

 

Abstract:

In this workshop, we'll take a dive into MLOps CI/CD + CT pipeline automation. Part 1, we’ll focus on how to put together a continuous ML pipeline to train, deploy, monitor, and retrain your models. Part 2 will focus on automations and production-first insights to detect and resolve issues faster.
This hand-on build will be done with the Google Vertex platform, Superwise model observability, and retraining notebooks. Ahead of the event you’ll get access to all the platforms, environments, and resources we’ll use so you can follow along during the session.

What You'll Learn:

- How to build a continuous MLOps stack - Platform and tool alternatives for each step
- Considerations for scaling up
- Production-first insights and automations

Workshop: A Guide to Putting Together a Continuous ML Stack

Co-Presenter: Itay Ben Haim

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Savin Goyal

Co-Founder and CTO, Outerbounds

Savin is the co-founder and CTO of Outerbounds where he focuses on building generalizable infrastructure to accelerate the adoption and impact of ML in enterprises. Previously, he was at Netflix where he built Metaflow, a popular OSS ML platform. In his previous life, he obtained a bachelors degree in CS from IIT Delhi.

Abstract:

There is a pressing need for tools and workflows that meet data scientists where they are. This is also a serious business need: How to enable an organization of data scientists, who are not software engineers by training, to build and deploy end-to-end machine learning workflows and applications independently. In this talk, we discuss the problem space and the approach we took to solving it with Metaflow, the open-source framework we developed at Netflix, which now powers hundreds of business-critical ML projects at Netflix and other companies from bioinformatics and drones to real estate. We wanted to provide the best possible user experience for data scientists, allowing them to focus on parts they like (modeling using their favorite off-the-shelf libraries) while providing robust built-in solutions for the foundational infrastructure: data, compute, orchestration, and versioning.

What You'll Learn:

In this talk, you will learn about
- What to expect from a modern ML infrastructure stack.
- Using tools such as Metaflow to boost the productivity of your data science organization, based on lessons learned from Netflix and many other companies.
- Deployment strategies for a full stack of ML infrastructure that plays nicely with your existing systems and policies.

Talk: Human-Friendly, Production-Ready Data Science

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Kevin Stumpf

Co-Founder and CTO, Tecton

Kevin Stumpf co-founded Tecton and is the CTO, where he leads the engineering team that is building a next-generation feature store for Operational Machine Learning. Kevin and his co-founders built deep expertise in Operational ML platforms while at Uber. They created the Michelangelo platform that enabled Uber to scale from 0 to 1000's of ML-driven applications in just a few years.
Kevin holds an MBA from Stanford University and a Bachelor’s Degree in Computer and Management Sciences from Germany’s University of Hagen. Outside of work, Kevin is a passionate long-distance endurance athlete.

Abstract:

Deploying ML in production is hard, and data is often the hardest part. Unlike analytics pipelines, production ML pipelines need to process both historical data for training, and fresh data for online serving, often using streaming or real-time data sources. They must ensure training/serving parity, provide point-in-time correctness, and serve data with production service levels. These challenges are difficult to tackle with traditional ETL tools, and can often add weeks or months to project timelines.
Kevin learned this lesson first-hand while trying to scale ML initiatives at Uber. To solve these data challenges, his team built the industry’s first feature store as part of the Uber Michelangelo platform. It was instrumental in scaling Uber’s ML initiatives to thousands of models in production. It’s now used to power every aspect of Uber’s business: ride ETAs, demand forecasting, pricing, and restaurant recommendations.
Since those early days, feature stores have emerged as the tool of choice to solve the data challenges of operational ML. At their core, feature stores provide a simple solution to store, serve and share features.
However, solving the storage and serving problem is not enough. Teams still need to tackle the data transformation problem - which typically means creating bespoke data pipelines to process raw data into features in real-time. To solve the end-to-end data problem for ML, organizations need more than a feature store. They need a complete feature platform, which includes automated ML data pipelines to transform data from batch and real-time sources. These platforms enable data teams to easily build batch, streaming, and real-time features, and deploy them to production quickly and reliably.

What You'll Learn:

In this session, attendees will learn about the data challenges faced by ML teams at Uber, and how they were solved with feature stores.
They will learn the common architecture of feature stores, and how they fit in the MLOps stack to solve the challenges of building production ML features. They’ll also learn the limitations of most feature stores, and why data teams really need a complete feature platform that can also automate ML data pipelines and process data from batch, streaming, and real-time sources.

Talk: Building Production-Ready ML Features with Feature Stores

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Rafael Pierre

Solutions Architect Data Science Expert, Databricks

Rafael Pierre is a Solutions Architect at Databricks, the founders of open source platforms such as Apache Spark, MLflow and Delta.
He holds a Bachelor’s Degree in Computer Science and has more than a decade experience in software development and architecture for Fortune 500 companies in mission critical, data intensive fields, such as the stock exchange, high frequency trading, IoT & Telematics. He also holds a Master’s Degree from the University of Amsterdam and extensive experience in delivering cloud, data & AI solutions at scale, designing and implementing end-to-end machine learning systems.

Abstract:

In this talk, you will learn how Databricks and MLflow provide a powerful set of features for implementing an end-to-end MLOps workflow.

What You'll Learn:

Experiment Tracking, Model Governance, Automatic CICD Pipelines with MLflow Webhooks

Talk: End-to-end MLOps MLflow and Databricks
Co-Presenter: Anastasiia Prokaieva

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Anastasiia Prokaieva

Specialist Solution Engineer – Data Science and MLOps, Databricks

Having 2 master's degrees in Science, I've been working on various Big Data areas from analysing data from Big Hadron Collider to satellite imageries on greenhouse emissions. Prior to Databricks worked as a Environmental Research scientist and as a Data Scientist in a French FinTech constructing proxies for macro-economic and extra-financial indicators based on alternative data using AI.
At Databricks I help our customers to accelerate their end-to-end Data Science projects by providing best practice approaches on MlOps with MlFlow, Spark and Delta and by delivering workshops and private ILT trainings created by Databricks team.

Abstract:

In this talk, you will learn how Databricks and MLflow provide a powerful set of features for implementing an end-to-end MLOps workflow.

What You'll Learn:

Experiment Tracking, Model Governance, Automatic CICD Pipelines with MLflow Webhooks

Talk: End-to-end MLOps MLflow and Databricks
Co-Presenter: Rafael Pierre

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Subhabrata Das

ML Engineer, JPMorgan Chase & Co.

Subhabrata Das has an ME from Cornell University and PhD from Columbia University, New York in the area of Computational Physics. He has worked at Avantor and Ally Bank prior to joining JP Morgan Chase AI/ML team. He had also consulted for Bank of America, Microsoft and Consumers Energy earlier. His research interests are in Machine Learning, Deep Learning and Natural Language Processing.

Abstract:

To a large extent, power outage forecasting is a daunting task because it depends on factors, such as weather, animal, tree, birds, load etc. Those factors generate a lot of fluctuations to outage data. As a type of RNN, LSTM has a good performance on processing time series data as well as some nonlinear and complex problems like stock price forecasting, fluctuations in energy consumption, demand response, traffic management etc. To explore more accurate power outage forecasting approach, in this paper, a new hybrid model based on Principal Component Analysis (PCA), Poisson regression (PR), Seq2Seq and Adam optimized LSTM neural network, denoted as PCA-PR-Seq2Seq-Adam-LSTM, is proposed. After the PCA, nonlinear sequence of power outage can be framed and processed data will have a more stable variance, and the combination of Adam, one of efficient stochastic gradient-based optimizers, and LSTM can capture appropriate behaviors precisely for the power failure. This study presented four cases to verify the performance of the hybrid model, and the dataset from Michigan area were adopted to illustrate the excellence of the hybrid model. The results show that the proposed model can significantly improve the prediction accuracy

What You'll Learn:

Applications of Machine learning and Deep learning in Power Utility Industry

Talk: A Hybrid Deep Learning Model for Power Outage Prediction

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Sree Gowri Addepalli

Senior AI Engineer, Target

Sree Gowri Addepalli is currently a Senior AI Engineer with the Advanced Machine Learning group in the AI Computer Vision Organization. This team is responsible for researching and developing cutting edge Computer Vision products in the digital space for Target. Sree Gowri Addepalli graduated from New York University’s Courant Institute of Mathematical Sciences with a Master’s in Computer Science. She has actively been part of academic research in the field of Computer Vision at NYU Center for Data Science. She has further gained industrial research experience as an intern at Amgen and Etsy while working on search problems. Prior to her masters, she held software engineering experience with Diebold Nixdorf in Mumbai after graduating from Mumbai University, India with a Bachelor’s in Computer Engineering. She likes ideating and building products and won the R&D hackathon at Diebold Nixdorf, 2018, among internal teams worldwide apart from organizing the NYUHack, 2019 at a global scale by serving as chair. She has previously gained scholarships to attend conferences like ECCV, CVPR, GHC to keep herself updated with the latest research. Her current research focus is computer vision and building machine learning systems in production. Apart from academics, she is a program manager with Indian Women in Computing where she works with AnitaB.org and various companies in helping to push the reach of STEM, specifically Artificial Intelligence, to the diverse sectors of society. She also founded the `Lean in Circle` in Collaboration with NYU women in computing.

Abstract:

Machine learning models drive some of the most important business decisions at Target. Therefore, monitoring the model behavior in production is of utmost importance. Using behavioral insights from internal models, Target has built a monitoring capability as a component of model observability for models deployed in production for a Visual Discovery application. This ensures that these models remain relevant and meet the desired performance in production while giving scientists the ability to understand and improve the quality of models in production.

What You'll Learn:

PRESENTATION DELIVERY TALKING POINTS:
• THE PROBLEM:
o How do we ensure that a machine learning model performs as expected in production over time? o How can you make ML models more observable? o Why is it important to solve this problem?
• THE SOLUTION:
o Overview of model monitoring framework architecture.
o Brief explanation of all components in the framework library.
o Ease of plugging in the framework to any machine learning application and infrastructure.
o Accessibility and availability to various machine learning applications across Target.
o Root cause analysis using evaluation metrics to improve model observability.
• DEMO:
o Showcase the usage of model monitoring framework plugged in as a library in a visual discovery application at Target.
o Components of model monitoring framework interacting with Target internal infrastructure to provide monitoring, visualizing and alerting capabilities.
• OUTCOME FOR THE ENTERPRISE:
o Capability to proactively monitor a machine learning model input and predictions thereby ensuring robust machine learning systems at Target.
o Model monitoring framework was made available across Target as a python library and various machine learning teams were able to easily plug in the library into their respective machine learning applications seamlessly with Target infrastructure.
o The custom-built solution over third party tools to ensure seamless integration with Target tech stack and provide machine learning teams with the ability to build custom features and evaluation metrics to understand model performance in production.

Talk: Model Monitoring: Ensure Robust Machine Learning Systems in Production
Co-Presenter: Vishal Vijay Shanker

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Dan Fu

PhD Student, Stanford University

Dan Fu is a PhD student in the Computer Science Department at Stanford University, where he is co-advised by Christopher Ré and Kayvon Fatahalian. His research focuses on understanding the principles behind why machine learning methods work and using that understanding to build the next generation of ML systems. He is supported by a Department of Defense NDSEG fellowship.

Abstract:

Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length. Approximate attention methods have attempted to address this problem by trading off model quality to reduce the compute complexity, but often do not achieve wall-clock speedup. We argue that a missing principle is making attention algorithms IO-aware -- accounting for reads and writes between levels of GPU memory. We propose FlashAttention, an IO-aware exact attention algorithm that uses tiling to reduce the number of memory reads/writes between GPU high bandwidth memory (HBM) and GPU on-chip SRAM. We analyze the IO complexity of FlashAttention, showing that it requires fewer HBM accesses than standard attention, and is optimal for a range of SRAM sizes. We also extend FlashAttention to block-sparse attention, yielding an approximate attention algorithm that is faster than any existing approximate attention method. FlashAttention trains Transformers faster than existing baselines: 15% end-to-end wall-clock speedup on BERT-large (seq. length 512) compared to the MLPerf 1.1 training speed record, 3× speedup on GPT-2 (seq. length 1K), and 2.4× speedup on long-range arena (seq. length 1K-4K). FlashAttention and block-sparse FlashAttention enable longer context in Transformers, yielding higher quality models (0.7 better perplexity on GPT-2 and 6.4 points of lift on long-document classification) and entirely new capabilities: the first Transformers to achieve better-than-chance performance on the Path-X challenge (seq. length 16K, 61.4% accuracy) and Path-256 (seq. length 64K, 63.1% accuracy).

What You'll Learn:

We present FlashAttention, a new fast and memory-efficient algorithm for exact attention. Our key insight is that attention is bottlenecked by slow memory accesses. We'll go over how we reduce the compute and memory footprint -- resulting in 2-4X faster training with 10-20X less memory, and enabling Transformers to scale up to sequences of length 64K.

Talk: FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

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Gideon Mendels

CEO & Founder, Comet

Gideon Mendels is CEO and Founder of Comet, an ML platform provider. He led a team that trained and deployed more than 50 NLP models in 15 languages as founder of GroupWize. He also worked on hate-speech and deception detection at Google, and he trained and put into production deep learning classifiers for 500 languages at Columbia University jointly with IBM Research.

Abstract:

While ML model development is a challenging process, the management of these models becomes even more complex once they're in production. Shifting data distributions, upstream pipeline failures, and model predictions that affect the very datasets they’re trained on can create thorny feedback loops between development and production. In this talk, Gideon Mendels, CEO and founder of Comet, will share industry examples of production ML systems.

What You'll Learn:

* Why naive ML workflows that don’t take the development-production feedback loop into account break down
* System design principles that will help manage these feedback loops more effectively
* Industry case studies where teams have applied these principles to their production ML systems

Talk: ML System Design for Continuous Experimentation

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Zoya Bylinskii

Research Scientist, Adobe Inc.

Zoya Bylinskii, Ph.D., is a Research Scientist in the Creative Intelligence Lab at Adobe Research as of 2018. Previously, she was an affiliate researcher at the Massachusetts Institute of Technology and at the Harvard Institute of Applied Computational Science. Zoya received her Computer Science Ph.D. and M.Sc. from MIT in 2018 and 2015, respectively, and an Hon. B.Sc. in Computer Science and Statistics, with a focus on Artificial Intelligence, from the University of Toronto in 2012. Zoya is a 2018 EECS Rising Star, 2016 Adobe Research Fellow, 2014-2016 NSERC Postgraduate Scholar, 2013 Julie Payette Research Scholar, and 2011 Anita Borg Scholar. Zoya works at the interface of human perception & cognition, computer vision, and human-computer interaction on applications in design, photography, and readability.

Abstract:

Dr. Bylinskii has pioneered the use of cognitive and perceptual science methods to create A.I. applications for graphic design, photography, and digital text that can automatically adapt and respond to users’ needs. Operating at the intersection of cognitive science & computer science, her work on visual attention and memorability has been recognized by interdisciplinary communities for driving both our understanding of human perception and the development of perceptually-guided algorithms. In this talk, Dr. Bylinskii will give you a taste of what we know about human memory & attention and how these insights can be applied to the creation of ML datasets, design of algorithms, and evaluation of AI tools.

What You'll Learn:

- What people tend to attend to, remember, and find important in photos, designs, and data visualizations
- How human perception & cognition data can be collected (i.e., crowdsourced at scale)
- The extra edge that ML algorithms that incorporate perception & cognition insights can have

Talk: Perception & Cognition Insights for ML

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Navdeep Gill

Engineering Lead, H2O.ai

Navdeep is an Engineering Lead at H2O.ai where he leads a team of researchers and engineers working on various facets of Responsible AI. He also leads science and product efforts around explainable AI, ethical AI, model governance, model debugging, interpretable machine learning, and security of machine learning. Navdeep previously focused on GPU accelerated machine learning, automated machine learning, and the core H2O-3 platform at H2O.ai.
Prior to joining H2O.ai, Navdeep worked as a Senior Data Scientist at Cisco and as a Data Scientist at FICO. Before that Navdeep was a research assistant in several neuroscience labs at the following institutions: California State University, East Bay, Smith Kettlewell Eye Research Institute, University of California, San Francisco, and University of California, Berkeley.
Navdeep graduated from California State University, East Bay with a M.S. in statistics with an emphasis on computational statistics, a B.S. in statistics, and a B.A. in psychology with a minor in mathematics.

Abstract:

Artificial intelligence and machine learning present significant opportunities to businesses. To reap the full benefits of ML, organizations need to trust the algorithms and incorporate them in their existing workflows. This presentation outlines a set of actionable best practices for people, process, and technology that not only enables incorporation of machine learning in business applications, but also ensures that it is done in a responsible manner for the business, and for the society at large. The goal is to increase adoption of machine learning in businesses by incorporating the appropriate governance, driving trust, efficiency, and business results.

What You'll Learn:

AI governance is needed for all organization to fully harness the application of AI

Talk: Security Audits for Machine Learning Attacks
Co-Presenter: Abhishek Mathur

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Yaron Singer

CEO & Co-Founder, Robust Intelligence

Yaron is the CEO of Robust Intelligence and Professor of Computer Science and Applied Math at Harvard. Yaron is known for breakthrough results in machine learning, algorithms, and optimization. Previously, Yaron worked at Google Research and obtained his PhD from UC Berkeley.

Abstract:

AI is eating the world, but corrupted data, drift, biased decisions, liabilities, and malicious actors regularly cause ML models to fail when making critical business decisions. This introduces risk to businesses, and importantly the people, affected by these failing models. Companies try to limit this by constantly monitoring dashboards and firefighting errors, This is incredibly time intensive, doesn’t get at the root of the problem, and can actually erode model accuracy. These reasons make it impossible to confidently apply ML at scale.
To solve this challenge and awaken the true force of AI, companies need to achieve ML Integrity. ML Integrity is the assurance of model quality, validity, and reliability. It is only realized when such considerations are applied to every stage of the ML lifecycle - from model development to production. Once companies have ML Integrity, intelligent applications can be developed with confidence and at greater velocity.

What You'll Learn:

Attendees will learn how to instill integrity into their ML models by implementing proactive measures throughout the lifecycle, from development to production. We’ll discuss what attendees should expect from a ML Integrity offering and review several use cases.

Talk: ML Integrity: The Quest to Awaken the True Force of AI

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Alex Kim

ML Engineer, Iterative

Alex’s past work experience involved solving data science and machine learning problems in various domains: physics, aerospace, telemetry/log analytics, image, and video processing.
In the last couple of years, he became increasingly interested in the engineering side of ML projects: processes and tools needed to go from an idea to a production solution. Currently, he works as an MLOps Solutions Engineer at Iterative.ai, helping customers extract the most value from the Iterative ecosystem of tools.

Abstract:

In the last few years, training a well-performing Computer Vision (CV) model in Jupyter Notebooks became fairly straightforward if you use pretrained Deep Learning models and high-level libraries that abstract away much of the complexity (fastai, keras, pytorch-lightning are just a few examples).
The hard part is still incorporating this model into an application that runs in a production environment bringing value to the customers and our business.
A typical ML project lifecycle goes through 3 phases:
• Active exploration or proof-of-concept phase. Here we try many different approaches to data preprocessing, model architectures and hyper-parameter tuning with the goal to finally settle on the most promising combination
• Application development phase. Here we build all the “plumbing” around the model, that is: getting input data, massaging it into the right format, passing it to the model and, finally, serving the model’s output. The goal here is to end up with a version of an end-to-end application that is robust and well-performing enough to be considered for deployment to the production environment.
• Production deployment phase. Here we promote a well-tested version of the application from the development environment to the production environment.
In this talk, I’ll describe an approach that streamlines all three phases. As our demo project, I’ve selected a very common deployment pattern in CV projects: a CV model wrapped in a web API service. Automatic defect detection is an example problem I am addressing with this pattern.
I assume the target audience of this talk to be technical folks (e.g. Software Engineers, ML Engineers, Data Scientists) who are familiar with the general Machine Learning concepts, Python programming, CI/CD processes and Cloud infrastructure.

What You'll Learn:

- How to quickly configure a remote development environment with TPI: write code locally while executing on a remote machine with a GPU
- How to version large datasets and models with DVC
- When it’s the right time to move from Jupyter notebooks to ML pipelines and how to do that with DVC
- Why it’s beneficial to integrate CI/CD workflows into your model development process and how to do that with CML
- How to manage experiments and collaborate on ML projects using Iterative Studio

Talk: Best MLOps Practices for Building End-to-End Deep Learning Projects

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Vishal Vijay Shanker

Lead Data Scientist, Target

Vishal is currently a Lead Data Scientist at Target leading the engineering efforts for Advanced Machine Learning - Visual Discovery team, focusing on Deep learning model deployment and inference pipelines and general MLOps. Through his prior work experience at Intel and Seagate, he has worked in several areas of Computer Science such as Web application development, Data warehousing, Big Data engineering, Storage devices and Firmware engineering. His passion is to research cutting edge Deep Learning architectures and quantize complex Deep Learning models for memory and performance so they can be used to solve real time problems in the AI space.

Abstract:

Machine learning models drive some of the most important business decisions at Target. Therefore, monitoring the model behavior in production is of utmost importance. Using behavioral insights from internal models, Target has built a monitoring capability as a component of model observability for models deployed in production for a Visual Discovery application. This ensures that these models remain relevant and meet the desired performance in production while giving scientists the ability to understand and improve the quality of models in production.

What You'll Learn:

PRESENTATION DELIVERY TALKING POINTS:
• THE PROBLEM:
o How do we ensure that a machine learning model performs as expected in production over time? o How can you make ML models more observable? o Why is it important to solve this problem?
• THE SOLUTION:
o Overview of model monitoring framework architecture.
o Brief explanation of all components in the framework library.
o Ease of plugging in the framework to any machine learning application and infrastructure.
o Accessibility and availability to various machine learning applications across Target.
o Root cause analysis using evaluation metrics to improve model observability.
• DEMO:
o Showcase the usage of model monitoring framework plugged in as a library in a visual discovery application at Target.
o Components of model monitoring framework interacting with Target internal infrastructure to provide monitoring, visualizing and alerting capabilities.
• OUTCOME FOR THE ENTERPRISE:
o Capability to proactively monitor a machine learning model input and predictions thereby ensuring robust machine learning systems at Target.
o Model monitoring framework was made available across Target as a python library and various machine learning teams were able to easily plug in the library into their respective machine learning applications seamlessly with Target infrastructure.
o The custom-built solution over third party tools to ensure seamless integration with Target tech stack and provide machine learning teams with the ability to build custom features and evaluation metrics to understand model performance in production.

Talk: Model Monitoring: Ensure Robust Machine Learning Systems in Production
Co-Presenter: Sree Gowri Addepalli

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Adi Hirstein

VP Product, Iguazio

Adi Hirschtein brings 20 years of experience as an executive, product manager and entrepreneur building and driving innovation in technology companies. As the VP of Product at Iguazio, the MLOps platform built for production and real-time use cases, he leads the product roadmap and strategy.
His previous roles spanned technology companies such as Dell EMC, Zettapoint and InfraGate, in diverse positions including product management, business development, marketing, sales and execution, with a strong focus on machine learning, database and storage technology. When working with startups and corporates, Adi’s passion lies in taking a team’s ideas from their very first day, through a successful market penetration, all the way to an established business.
Adi holds a B.A. in Business Administration and Information Technology from the College of Management Academic Studies.

Abstract:

Snowflake is one of the most popular data clouds today. With so much data at your fingertips, how do you harness it to create business value?
Most data science teams start their AI journey from what they perceive to be the logical beginning: building AI models using manually extracted datasets. Operationalizing machine learning, in the sense of considering all the requirements of the business; handling online and federated data sources, scale, performance, security, continuous operations, etc. comes as an afterthought, making it hard and resource-intensive to create real business value with AI.
But now – There is a better solution to work in an automated, production-ready environment and cut your time to production from 12 months to 30 days (https://www.iguazio.com/customers/)!
The Iguazio MLOps platform now comes with a built-in Snowflake connector, enabling enterprises to seamlessly access the Snowflake Data Cloud to build, store and share features that are ready for use in machine learning applications on Iguazio, and automate their ML pipeline end to end.
In this session, we will describe the challenges in operationalizing machine & deep learning. We’ll explain the production-first approach to MLOps pipelines - using a modular strategy, where the different components provide a continuous, automated, and far simpler way to move from research and development to scalable production pipelines. Without the need to refactor code, add glue logic, and spend significant efforts on data and ML engineering. We will cover various real-world implementations and examples, and discuss the different stages, including automating feature creation using a feature store, building CI/CD automation for models and apps, deploying real-time application pipelines, observing the model and application results, creating a feedback loop and re-training with fresh data.
We’ll demonstrate how to use Iguazio & Snowflake to create a simple, seamless, and automated path to production at scale!

What You'll Learn:

In this session, we will describe the challenges in operationalizing machine & deep learning. We’ll explain the production-first approach to MLOps pipelines - using a modular strategy, where the different components provide a continuous, automated, and far simpler way to move from research and development to scalable production pipelines. Without the need to refactor code, add glue logic, and spend significant efforts on data and ML engineering. We will cover various real-world implementations and examples, and discuss the different stages, including automating feature creation using a feature store, building CI/CD automation for models and apps, deploying real-time application pipelines, observing the model and application results, creating a feedback loop and re-training with fresh data.

Talk: How to Build an Automated ML Pipeline with a Feature Store using Iguazio & Snowflake

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Mark Moyou

Senior Data Scientist, NVIDIA

Dr. Mark Moyou is a Senior Data Scientist at NVIDIA on the Retail team focused on enabling scalable machine learning for the nation's top Retailers. He is the Conference Director of the Southern Data Science Conference in ATL, GA and host the the Caribbean Data Science Podcast. Before NVIDIA, he was a Data Science Manager in the Professional Services division at Lucidworks, and Enterprise Search and Recommendations company. Prior to Lucidworks, he was a Data Scientist at Alstom Transportation where he applied Data Science to the Railroad Industry. Mark is quite active in his fitness pursuits, so feel free to ask about that topic.

Abstract:

This talk will cover getting started and best practices for doing XGBoost training and inference on GPUs. Why should you consider using GPUs with XGBoost? When training XGBoost models, finding the optimal hyperparameters can take significant time if model training times are slow on CPUs (especially at scale). By leveraging both single and multi-GPUs, we can often see 10x+ speedups in model training, enabling the discovery of optimal hyperparameters in much less time.
We will show how to train XGBoost models across single and multiple GPUs leveraging Dask (Advanced Parallelism in Python). After model training, we will show how to get the best performance at inference time on CPU and GPU by leveraging the open-source RAPIDS Forest Inference Library (FIL) and NVIDIA's open-source inference server, Triton.
Results show an increase in inference throughput up to 9.4x on CPU and 240x on GPU; reduction in compute time by 88% and cost savings up to 56% on GPU vs. CPU. Notebooks and slides will be shared.

What You'll Learn:

How to use XGBoost on GPUs along with some feature engineering best practices

Talk: Leveraging GPUs for Faster XGBoost Training and Inference at Reasonable and Large Scale

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Abhishek Mathur

Product Lead, Responsible AI, H2O.ai

Abhishek is the Product Lead for AI Governance, Responsible AI, and MLOps products at H2O.ai. He focuses on removing friction from organizations (regulatory, compliance, transparency, fairness, audit, accountability, amongst others) in adoption and realizing value from machine learning. Abhishek has previously led product teams in developing NLP (conversational AI), computer vision, and internet of things platforms. He enjoys teaching product management foundations to aspiring PMs, and is a web3 enthusiast.

Abstract:

Artificial intelligence and machine learning present significant opportunities to businesses. To reap the full benefits of ML, organizations need to trust the algorithms and incorporate them in their existing workflows. This presentation outlines a set of actionable best practices for people, process, and technology that not only enables incorporation of machine learning in business applications, but also ensures that it is done in a responsible manner for the business, and for the society at large. The goal is to increase adoption of machine learning in businesses by incorporating the appropriate governance, driving trust, efficiency, and business results.

What You'll Learn:

AI governance is needed for all organization to fully harness the application of AI

Talk: Security Audits for Machine Learning Attacks
Co-Presenter: Navdeep Gill

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Alon Gubkin

Aporia, CTO

Alon Gubkin is the CTO of Aporia, a customizable monitoring platform that enables data science teams to build their own monitoring solution for ML models in production. Writing code from the age of seven, Alon is passionate about programming, machine learning, biology, video games, and software architecture.

Abstract:

Building your ML Platform on top of Kubernetes rocks. In this session we will discuss the core principles of Kubernetes and cloud native applications, as well as Infrastructure as Code and why MLOps benefits from it so much. We'll also demonstrate a reference architecture to help you get started.

What You'll Learn:

How to get started with building an ML Platform on top of K8s

Talk: Why Cloud Native MLOps Rocks

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David Hershey

Senior Solutions Architect, Tecton

David Hershey is a Solutions Architect at Tecton, where he helps customers implement feature stores as part of their stack for Operational ML. Prior to Tecton, David was a Solutions Engineer at Determined AI and a Product Manager for Ford’s ML platform. David holds an MS in Computer Science from Stanford University and BS in Aerospace Engineering from the University of Michigan.

Abstract:

Tecton integrates with Snowflake and enables data teams to process ML features and serve them in production quickly and reliably, without building custom data pipelines.
In this workshop, David shows how to build an end-to-end movie recommendation system using a feature platform in three stages:
- Batch, daily computed, recommendations
- Online recommendations using batch features
- Online recommendations using real-time features

What You'll Learn:

You will learn how to:
- Build new features using Tecton’s declarative framework
- Automate the transformation of batch data directly on Snowflake
- Automate the transformation of real-time data using Snowpark
- Create training datasets from data stored in Snowflake
- Serve data online using DynamoDB or Redis

Workshop: Building a Movie Recommendation System on Tecton with Snowflake

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Victor Sonck

Evangelist, ClearML

Victor is an enthusiastic machine learning engineer and tinkerer. Having worked on dozens of machine learning projects in the world of consultancy, he’s now more focused on making inspiring, entertaining and questioning content. Making youtube videos about silly machine learning projects or writing more serious blogposts on how to deploy them. He loves public speaking and is want to share the magic of machine learning with everyone willing to listen.

Abstract:

Data scientists are usually not trained to go further than their analyses, however in order to get to a more mature AI infrastructure that can support more models in production, additional steps will have to be taken. Experiment management and Data versioning are very important first steps in the direction of the "MLops" way of working. Done properly, they can serve as a foundation to build more advanced systems on top, such as pipelines, remote workers and advanced automation. When a data scientist can include this way of working into their day-to-day, they have a very powerful tool in hand to raise the success rate of their models and analyses.

What You'll Learn:

A set of tools, tips, tricks and example workflows you can use in your own life to help alleviate common data science challenges.

Workshop: The Importance of Experiment Tracking and Data Traceability

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Jimmy Whitaker

Chief Scientist of AI, Pachyderm

Jimmy Whitaker is the Chief Scientist of AI at Pachyderm. He focuses on creating a great data science experience and sharing best practices for how to use Pachyderm. When he isn’t at work, he’s either playing music or trying to learn something new, because “You suddenly understand something you’ve understood all your life, but in a new way.

Abstract:

Data-centric AI is bridging the gap between research and practice. Instead of optimizing our algorithms and architectures, pivoting to focus on data as the primary way to improve our machine learning models is yielding tremendous results. But this shift to data has left some gaps in our development process, and with this shift, we need to rethink how we develop AI from tooling to processes.

What You'll Learn:

In this presentation join us as we examine:
- Data Centric AI and how did we get here?
- Data as the new ‘Source Code’
- What are the practical steps towards Data Centric AI

Talk: Rethinking ML Development – A Data-Centric Approach

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Alex Kim

MLOps Solutions Engineer, Iterative

Alex is a Solutions Engineer at Iterative. His background is in physics, software engineering, and machine learning. In the last few years, he became increasingly interested in the engineering side of ML projects: specifically in the processes and tools needed to go from an idea to a production solution.

Abstract:

Learn how to manage and make your machine learning projects reproducible with an open-source tool DVC and its extension for VS Code. We will see how to track datasets and models, run, compare, visualize, and track machine learning experiments right in VS Code.

Talk: Machine Learning Experimentation with DVC and VS Code

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Nikita Demir

Founding Engineer, Galileo

Founding Engineer at Galileo 🔭 🌕 | Masters @ Stanford CS (AI/ML)

Abstract:

Come see how clean our standard public datasets really are! We'll cover a few of the most common patterns of data errors we've encountered on real world datasets.

What You'll Learn:

Data-centric AI, Data Errors, Mislabels, Class Overlap, Imbalance, Drift

Talk: Common ML Data Issues in Public Datasets

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Raymond Reed

Customer Success Data Scientist, WhyLabs

Ray is a Customer Success Data Scientist at WhyLabs, the AI Observability company. He has a long held passion for machine learning and loves helping customers save time and money by monitoring their ML systems at scale. Ray was formerly a Senior Success Engineer at Datorama, a Salesforce Company, where he drove success for large enterprise customers with a focus on improving query performance across the company. With his spare time, Ray enjoys hiking, music, and more hiking.

Abstract:

As data pipelines become increasingly complex and data volumes grow at a dizzying rate, maintaining observability into your data and model health is more critical than ever. Each phase of a data pipeline introduces potential points of failure which are notoriously difficult and time consuming to detect and troubleshoot. To make matters more challenging, the world around us is constantly changing, and so is our data. Without proper observability into your data and model health, models can fail without warning, resulting in large costs to your business. Worse yet, they can fail silently and you won’t even know. With the rise of MLOps, researchers and ML practitioners have joined forces to develop methodologies for monitoring these systems for things like data drift, concept drift, and data quality degradation. However, this problem remains a unique one in the domain of computer vision.
In this talk, Ray Reed will explore the plethora of problems impacting data and model health for computer vision systems specifically, and construct an approach for detecting, debugging, and reacting to these issues at scale. In the case of tabular data, powerful monitoring capabilities can be achieved by collecting telemetric data such as missing value ratios, cardinality of discrete features, descriptive statistics, etc. For image and video data, however, metrics of interest must be derived.
Ray will discuss the types of metrics which must be tracked in order to answer the most important questions about image and video data health, and to successfully debug elusive problems faced in the computer vision domain. Lastly, he will discuss how proper visualization and anomaly detection can be introduced to dramatically reduce the time spent on these monitoring and debugging tasks, and to maintain observability into your computer vision system’s health at all times.

What You'll Learn:

The audience will learn about the various factors which threaten the performance of computer vision systems and how an appropriate monitoring solution can help get a handle on these.

Talk: Monitoring CV Systems: A Unique Solution to A Unique Problem

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Amir Servi

Head of Product, Superwise

Amir is Head of Product at Superwise the self-service model observability platform. With over 15 years in the technological space and vast experience in the AI ecosystem at both Deci.AI and AnyVision, Amir specializes in leading product and R&D teams to deliver bleeding-edge technology products, from computer visions, through neural network acceleration, all the way to reshaping MLOps and model observability for a high-scale, data-driven era.

Abstract:

Your DS team just let you know that instead of one model serving predictions for all your customers, from here on out each customer will have their own separate model. Now your MLOps team needs to support multiple models spanning, training, validation, serving, and monitoring - your pipeline just became x10 more complex than it was just a day ago, and scaling your architecture means more than just adding a replica.

What You'll Learn:

This session will cover architectural considerations for multi-tenancy in ML, best practices in traditional software engineering that can be copy/pasted over to MLOps, as well as new considerations unique to ML
- Why and when to use ML multi-tenancy
- Architecture pros and cons
- Observability and monitoring at high scale
- Security and compliance considerations

Talk: A Guide to Multi-Tenancy Architectures in ML

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Amber Roberts

Machine Learning Engineer, Arize AI

Amber is an Astronomer and Machine Learning Engineer. She comes to Arize from the Splunk ML Product Org where she built out the ML feature solutions as an ML Product Manager. Ambers' current role as a community-oriented Machine Learning Engineer looks to help teams across all industries build ML Observability into their productionalized AI environments.

Abstract:

Taking a model from research to production is hard — and keeping it there is even harder! As more machine learning models are deployed into production, it is imperative to have tools to monitor, troubleshoot, and explain model decisions. In this workshop attendees will implement ML observability firsthand in the Arize platform. Participants will monitor, surface, resolve, and improve model performance on a fraud model in production.

What You'll Learn:

Attendees will learn how to identify segments where a model is underperforming, troubleshoot and perform root cause analysis, and proactively monitor your model for future degradations.

Workshop: Troubleshooting your ML Models in Production

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Eddie Esquivel

Senior Solutions Architect, Tecton

Eddie Esquivel is a Solutions Architect at Tecton, where he helps customers implement feature stores as part of their stack for Operational ML. Prior to Tecton, Eddie was a Solutions Architect at AWS.

Abstract:

Tecton integrates with Snowflake and enables data teams to process ML features and serve them in production quickly and reliably, without building custom data pipelines.
In this workshop, David shows how to build an end-to-end movie recommendation system using a feature platform in three stages:
- Batch, daily computed, recommendations
- Online recommendations using batch features
- Online recommendations using real-time features

What You'll Learn:

You will learn how to:
- Build new features using Tecton’s declarative framework
- Automate the transformation of batch data directly on Snowflake
- Automate the transformation of real-time data using Snowpark
- Create training datasets from data stored in Snowflake
- Serve data online using DynamoDB or Redis

Workshop: Building a Movie Recommendation System on Tecton with Snowflake

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Gustaf Cavanaugh

Field Team, ContinualAI

Gus started his career working on data projects at large consulting firms before moving to ML startups. His focus is removing technical barriers and buzzwords that prevent data teams from getting their work done. He is a big fan of Python and very thankful for the open source community.

Abstract:

With the release of Python for Snowpark, the Snowflake cloud data platform now offers first class support for Python. This creates an exciting opportunity for ML teams. Whereas previously users would need to extract data from Snowflake to run python-based ML pipelines somewhere else (like Kubernetes), now everything can run in Snowflake itself. Used correctly, this reduces the friction from experimentation to deployment, enabling ML teams to deliver use cases faster with IT & Engineering’s blessing
As Snowpark for Python is still new, the ecosystem of documentation and tutorials for new users is catching up. This tutorial provides an opinionated quickstart to get users comfortable with building their own projects on Snowpark.
The tutorial flow is as follows:
Start from scratch with installation and config, progressing to typical core pandas operations used daily by data scientists Understand where code should run (in-database or locally) and when to use local compute, UDFs, and Stored Procedures to find the right balance of developer efficiency, speed, and cost
Get an introduction to MLOPs concepts and walk through sample implementations using code as well as popular vendor solutions such as Sagemaker and Continual

What You'll Learn:

- Practical skills with Snowpark’s Python API: how to use SnowPark dataframes, UDFs, and Stored Procedures to get your work done.
- Core performance vs cost tradeoffs: when and how to run code in-database or locally and which Snowpark tools to use.
- Minimum amount of MLOPs necessary regardless of language or framework to prevent failed ML projects: we’ll share implementations with open source frameworks as well as vendor solutions like AWS Sagemaker and Continual

Workshop: Beginner’s Guide to Operational ML with Python for Snowpark

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Rolando Garcia

PhD Candidate, UC Berkeley

Rolando is an aspiring professor and a Ph.D. candidate at UC Berkeley working in the RISE Lab. His graduate research focus has been Experimentation in MLOps. Specifically, processes and tools for ML engineers to iterate on models and data with high velocity. Rolando is the primary developer and maintainer of flor, a record-replay system for hindsight logging & ML training (in Python). Rolando is advised by Joe Hellerstein, and am a fortunate recipient of UC Berkeley Chancellor's Fellowship and the NSF Graduate Research Fellowship. Rolando has served industry in an ML engineering capacity. First at Amazon Search in 2018 and then as a developer for a startup.

Abstract:

Organizations rely on machine learning engineers (MLEs) to operationalize ML, i.e., deploy and maintain ML pipelines in production.
The process of operationalizing ML, or MLOps, consists of a continual loop of
(i) data collection and labeling,
(ii) experimentation to improve ML performance,
(iii) evaluation throughout a multi-staged deployment process, and
(iv) monitoring of performance drops in production.
When considered together, these responsibilities seem staggering—how does anyone do MLOps, what are the unaddressed challenges, and what are the implications for tool builders?
We conducted semi-structured ethnographic interviews with 18 MLEs working across many applications, including chatbots, autonomous vehicles, and finance. Our interviews expose three variables that govern success for a production ML deployment: Velocity, Validation, and Versioning. We summarize common practices for successful ML experimentation, deployment, and sustaining production performance. Finally, we discuss interviewees’ pain points and anti-patterns, with implications for tool design.

What You'll Learn:

ML engineering is very experimental in nature. Operationalizing model evaluation requires an active organizational effort. Non-ML rules and human-in-the-loop practices keep models reliable in production.

Talk: Operationalizing Machine Learning: An Interview Study

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Daniel Siryakov

Product Manager, Comet

Daniel is a Senior Product Manager at Comet ML in charge of Experiment Management, Artifacts and Model Registry. Prior to Comet, Daniel built from the ground up and led PNC's systematic investment strategies - building and deploying models while designing the technology to support their management at scale. When Daniel isn't interviewing practitioners building cool AI projects he enjoys cooking and trying to beat AlphaGo.

Abstract:

Computer Vision problems are a hard nut to crack especially when you have to debug millions of images across multiple experiments and runs. Comet provides a set of tools and features in its platform that makes it very intuitive for Computer Vision Engineers to visualize unique images, search to compare images, and show multiple experiments in an easy to use interface with as little as two-lines of code. Come to our session to learn more.

What You'll Learn:

How to use a new feature called Image Panel to visualize, search and debug across many experiments

Talk: Multiply Your Image Search & Testing Capabilities using Comet

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Yaoshiang Ho

Head of Product, Masterful AI

Yaoshiang Ho is the head of product at Masterful AI. He focuses on building tools to help developers unlock the power of deep learning. He’s held roles in software engineering, product management, and business functions.

Abstract:

This talk covers a roadmap of action items for data scientists and software engineers to add CV capabilities to their products. For the vast majority of applications, there’s a simpler and easier approach to adding computer vision capabilities than what you might gather from academic courses and developer newsletters. First, taking a data-centric view. This means that developers should spend more of their time on acquiring and labeling high quality training data, rather than spend time on the latest model architecture or advanced training algorithms. Second, taking full advantage of the plethora of off-the-shelf tools to make the process of building and deploying deep learning models far easier than even a year ago. Finally, the traditional academic focus on top-1 accuracy belies many other topics that must be addressed to build a successful application with deep learning, including deployment, testing scenarios, explainability, and non-deterministic behavior.

What You'll Learn:

A realistic, practical approach to build applications that include computer vision capabilities. It will contrast with the approach that most online resources recommend, which are overly academic, foundational, and architecture-centric.

Talk: Practical Approaches to Computer Vision

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Thaddeus Fortenberry

Senior Principal Computer Architect, Tenstorrent

Thaddeus’ career has spanned Microsoft, AMD, Tesla, Apple and now Tenstorrent. He designed the Autopilot training cluster and has several patents addressing scaled ML pipelines and hardware resource management. He’s also focused on high-speed parallel storage systems and cluster fabrics. At Tenstorrent he's working on next-generation cluster design, edge-to-backend pipelines, and heterogeneous architecture.

Abstract:

MLOps originated from the complexity of running and managing ML cluster environments. The industry has been moving from R&D to large-scale production deployments. Today we’re able to manage and track development, orchestrate resources, and automate pipelines. We’re starting to segment portions of ML workloads that run on the most appropriate hardware. Tomorrow’s automation will evolve to accurately schedule resources reflecting specific workload requirements.

What You'll Learn:

Why MLOps is important today and opportunities to increase cluster efficiency.

Talk: The Exciting Evolution of MLOps

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