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MLOps World est 2020

Toronto Machine Learning Society est. 2017

Talk: MLOps on Highly Sensitive Data - Strict Confinement, Confidential Computing and Tokenization Protecting Privacy

Presenters:
Andreea Munteanu, AI/ML Product Manager, Canonical | Maciej Mazur, Principal ML Engineer, Canonical

About the Speakers:
I am a Product Manager at Canonical, leading the MLOps area. With a background in Data Science in various industries, such as retail or telecommunications, I used AI techniques to enable enterprises to benefit from their initiatives and make data-driven decisions. I am looking to help enterprises get started with their AI projects and then deploy them to production, using open-source, secure, stable solutions.

I am a driven professional, passionate about machine learning and open source. I always look for opportunities to improve, both myself and people within the teams that I am part of. I enjoy sharing my knowledge, mentoring young professionals and having an educational impact in the industry.

Talk Track: Advanced Technical/Research

Talk Technical Level: 4/7

Talk Abstract:
MLOps is used in various organizations that operate on very sensitive datasets. For instance, pharmaceutical and life science companies handle human DNA samples, healthcare institutions training models on patient data, or highly regulated environments like telecom and financial companies. End users, machine learning engineers or data scientists can be concerned about cloud-native workloads would expose them more to software vulnerabilities, data leaks, or any lack of data protection measures. In reality, it’s just the opposite. This presentation will cover how you can improve compliance and security with features like Kubernetes strict confinement, blockchain-based tokenization and privacy-enhancing technologies like confidential computing. The talk will feature a case study by a life sciences company that created customized treatments using these technology building blocks. After the talk, you will understand how you can apply them yourself on cloud envorinments MLOps using Kubeflow.

What You’ll Learn:
In this talk, we will discuss how you can set up a secure foundation for machine learning with open-source building blocks. We will cover how confidential computing on the public cloud helps you address run time insecurity. You will then learn how Kubernetes’ strict confinement helps you get complete isolation, up to a minimum access level to the host resources. Finally, we will cover how tokenization can enable you to avoid data leaks, and allows at the same time achieving high system productivity. We will demonstrate how this works in practice with a life sciences use case powered by Charmed Kubeflow, an open-source community-driven end-to-end MLOps platform.

Talk: MLOps for Graph-Based Recommender Systems: Orchestrating Intelligent Connections

Presenter:
Aishwarya Reganti, Applied Scientist, Amazon

About the Speaker:
Aishwarya is an Applied Scientist in the Amazon Search Science and AI Org. She works on developing large scale graph-based ML techniques that improve Amazon Search Quality, Trust and Recommendations. She obtained her Master’s degree in Computer Science (MCDS) from Carnegie Mellon’s Language Technology Institute, Pittsburgh. She has over 6+ years of hands-on Machine Learning experience and 20+ publications in top-tier conferences like AAAI, ACL, CVPR, NeurIPS, EACL e.t.c. She has worked on a wide spectrum of problems that involve Large Scale Graph Neural Networks, Machine Translation, Multimodal Summarization, Social Media and Social Networks, Human Centric ML, Artificial Social Intelligence, Code-Mixing e.t.c. She has mentored several Masters and PhD students in the aforementioned areas. She has also served as a reviewer in various NLP and Graph ML conferences like ACL, EMNLP, AAAI, LoG e.t.c. She has worked with some of the best minds in both academia and industry through collaborations and internships in Microsoft Research, University of Michigan, NTU Singapore, IIIT-Delhi, NTNU-Norway, University of South Carolina e.t.c.

Talk Track: Advanced Technical/Research

Talk Technical Level: 4/7

Talk Abstract:
As graph-based recommender systems continue to gain prominence in large tech companies, the need for efficient management of machine learning operations (MLOps) becomes paramount. This talk highlights the specific challenges faced in operationalizing graph-based recommender systems and how MLOps addresses these complexities. We delve into techniques for efficient data ingestion, feature engineering, and model training within the graph context. We emphasize the significance of versioning and managing graph models, ensuring reproducibility and scalability in the MLOps pipeline.

What You’ll Learn:
The audience will learn about the unique advantages and challenges of implementing graph-based MLOps in recommender systems. They will gain insights into how graph structures enable more accurate recommendations. The talk will cover techniques for data preprocessing, feature engineering, and model training specific to graph data using the DGL library. Overall, attendees will gain a comprehensive understanding of the transformative potential of graph-based MLOps in building and managing recommender systems.

Talk: Stable Diffusion for Your Images: Custom Dream

Presenter:
Sandeep Singh, Head of Applied AI, Beans.AI

About the Speaker:
Sandeep Singh is a leader in applied AI and computer vision in Silicon Valley’s mapping industry, and he is at the forefront of developing cutting-edge technology to capture, analyze and understand satellite imagery, visual and location data.

With a deep expertise in computer vision algorithms, machine learning and image processing and applied ethics, Sandeep is responsible for creating innovative solutions that enable mapping and navigation software to accurately and efficiently identify and interpret features to remove inefficiencies of logistics and mapping solutions.

His work includes developing sophisticated image recognition systems, building 3D mapping models, and optimizing visual data processing pipelines for use in logistics, telecommunications and autonomous vehicles and other mapping applications.

With a keen eye for detail and a passion for pushing the boundaries of what’s possible with AI and computer vision, Sandeep’s leadership is driving the future of applied AI forward.

Talk Track: Advanced Technical/Research

Talk Technical Level: 6/7

Talk Abstract:
Welcome to the “Stable Diffusion for Your Images: Custom Dream” workshop! In this one-hour session, we will dive into the fascinating world of stable diffusion techniques for creating custom images.

Dreambooth is an innovative platform that allows users to unleash their creativity and transform ordinary images into extraordinary works of art. Through stable diffusion, we will explore how to manipulate and enhance images in a visually stunning and captivating way.

During the workshop, participants will learn the fundamentals of stable diffusion and its applications in image editing. We will cover various techniques, including color manipulation, texture enhancement, and image blending, to create visually striking and unique compositions.

Additionally, we will delve into the intricacies of Dreambooth’s user-friendly interface, providing hands-on demonstrations and step-by-step guidance. Participants will have the opportunity to experiment with different filters, effects, and settings, unleashing their artistic potential and transforming their photos into mesmerizing masterpieces.

Whether you are a professional photographer looking to add an extra flair to your work or an amateur enthusiast eager to explore new creative avenues, this workshop is designed to inspire and empower you. Join us for an hour of exploration, experimentation, and artistic expression as we unlock the potential of stable diffusion with Dreambooth.

What You’ll Learn:
Stable Diffusion is very easy for anybody to use and customize for the purpose at hand.

Talk: From Analytics to AI How Gen AI Can Unlock Data Insights and Transform Decision-Making

Presenter:
Shingai Manjengwa, Head of AI Education, ChainML

About the Speaker:
Shingai is the Head of AI Education at ChainML where she works with clients on AI education, adoption, and implementation. Shingai takes clients from an AI product idea to an affordable and scalable deployment. A seasoned data scientist, Shingai has a background in retail analytics, generating data insights to top tier manufacturers and retailers, and she has led technical education at the Vector Institute for AI. Shingai is also the founder of Fireside Analytics Inc., an online data science education company. Over half a million learners have taken her courses on platforms such as IBM and Coursera. Shingai sits on a number of boards and advisory committees, and she is an award-winning speaker and children’s book author.

Talk Track: Business Strategy

Talk Technical Level: 3/7

Talk Abstract:
In this talk, Shingai will explore the profound transition from traditional reporting, through advanced analytics, to AI-enabled teams. Shingai will introduce a framework for business leaders to leverage AI in their organizations to drive impact on key performance indicators.

Shingai will also emphasize the importance of setting clear team objectives aligned with core business values, illustrating how this alignment will distinguish companies in the AI era. She will share a model outlining the journey of implementing AI in business, from the initial idea to developing a proof-of-concept AI tool, while also addressing risk management strategies for AI.

By the conclusion of this presentation, attendees will gain a fresh perspective on how AI can reshape data insights and decision-making and how the responsible adoption of AI can have a transformative impact on business operations.

What You’ll learn:
Transition to AI: Acknowledge the transformative shift from traditional reporting to advanced analytics and AI-enabled teams as a strategic imperative for businesses.

AI Impact on KPIs: Recognize that AI has the potential to significantly impact key performance indicators (KPIs) in meaningful ways, and business leaders should explore its integration.

Alignment with Business Objectives: Emphasize the importance of setting clear team objectives that align seamlessly with core business values, positioning alignment as a key differentiator in the AI era.

AI Implementation Journey: Understand the journey of integrating AI into business operations, starting from the initial idea phase and progressing to the development of practical AI tools.

Risk Management: Acknowledge the importance of implementing robust risk management strategies to mitigate potential challenges associated with AI adoption.

Data Insights and Decision-Making: Grasp the potential of AI to reshape the landscape of data insights and decision-making, and its potential to revolutionize the way businesses operate in the future.

Talk: Unlocking Potential: A Fireside Chat on Generative AI, Reasoning, and Logic in Enterprise Innovation

Presenter:
Reuven Cohen, CTO Generative AI, EY Americas

About the Speaker:
Reuven Cohen is an influential technology leader with over three decades of experience, particularly recognized as the CTO, Generative AI at EY Americas. In this role, he is spearheading the Generative AI strategy, transforming the workforce of nearly 400,000 and serving a vast portfolio of Fortune 500 clients.

His pioneering work in cloud computing is equally noteworthy. He founded Enomaly Inc., a trailblazing cloud computing company, and coined the term “”infrastructure as a service”” in 2005. As an inaugural member of the Amazon Web Services advisory board and co-founder of CloudCamp in 2008, Reuven’s impact on the cloud computing field is undeniable.

In the realm of artificial intelligence, Reuven served as an alpha/beta tester for OpenAI, contributing significantly to the growth and innovation of AI programming and engineering.

At EY, Reuven’s visionary leadership, combined with his extensive experience in both AI and cloud computing, continues to drive technological advancements and organizational success. His commitment to innovation and collaboration has solidified his reputation as a trusted authority in the industry, making him an indispensable ally for businesses, governments, and technology enthusiasts alike.

Talk Track: Business Strategy

Talk Technical Level: 5/7

Talk Abstract:
In a world where Generative AI is shaping enterprise innovation, this fireside chat explores the integration of reasoning, logic, and cutting-edge techniques like Language Models (LLM) and mixture of experts tactics. Led by Reuven Cohen, CTO of Generative AI at EY Americas, the discussion uncovers how LLM fuels new chain-of-thought processes, while the mixture of experts approach optimizes decision-making within businesses.

Join us for a thought-provoking conversation that delves into the real-world applications of these technologies, offering valuable insights into their transformative impact on modern enterprises. This talk is a must for anyone interested in the intersection of human intellect, artificial intelligence, and the future of business innovation.

What You’ll learn:
Attendees will leave with a deeper understanding of how Generative AI, combined with innovative techniques like Language Models (LLM) and the mixture of experts tactics, is revolutionizing reasoning, logic, and decision-making within modern enterprises.

Talk: Building Computers for AI

Presenter:
David Bennett, Chief Customer Officer, Tenstorrent

About the Speaker:
I am currently the Chief Customer Officer (CCO) for Tenstorrent Inc., a Toronto based unicorn developing software, silicon and systems to run AI and ML faster than anyone else. We are also developing a full line up of RISC-V CPUs.

I recently left Tokyo, Japan where I ran Lenovo’s Japan operations including both the Lenovo brand and market leading NEC Personal Computer; the leader in Consumer, Commercial and Enterprise solutions, Lenovo is the largest PC manufacturer in Japan.

Previously, I ran AMD’s Asia based Global MNC accounts and was the Asia Pacific and Japan Mega Region Vice President where the team has achieved 12 quarters of consecutive YoY revenue and share growth. I also lead a global team driving >$600M a year in Commercial Client, Thin Client and Server CPU and Graphics processor revenue. We focus on an end-to-sale beginning with our OEM partners, right through to our end customers.

I am known for having exceptionally high bandwidth, a sales methodology based on creativity, passion and reliability, and for delivering results. I am constantly ranked top of our management surveys, and I believe that to survive and thrive we must know more than the competition about our products, the industry and our customers.

Fascinated by the future of technology and computing, I leverage my knowledge of hardware and software design and programming to predict trends and to shape our future product development and regional deployment strategies.

Talk Track: Business Strategy

Talk Technical Level: 3/7

Talk Abstract:
Tenstorrent is an innovative hardware architecture designed to enhance the efficiency and scalability of artificial intelligence (AI) workloads. Addressing the compute-intensive nature of modern AI applications, Tenstorrent leverages a unique grid-based architecture to enable efficient execution of both sparse and dense computations. Its dynamic code generation and execution capabilities allow flexibility across various AI models and algorithms. Furthermore, Tenstorrent emphasizes scalability, with the potential to deploy individual units in massive, interconnected networks for larger AI tasks. The system is designed to optimize power efficiency and performance, making it an ideal solution for advanced machine-learning jobs. Architectures like Tenstorrent will be crucial in harnessing its full potential as AI drives technological progress.

What You’ll learn:
The audience will learn about hardware for AI

Talk: Tales of Innovation Within a 100 Year Old Company

Presenters:
Naiel Samaan, Senior Product Owner, AI Platform, Ford Motor Company | Valmir Bucaj, AI/ML Platform Product Owner, Ford Motor Company

About the Speakers:
Naiel- As a seasoned product, people, and project manager, I thrive on tackling complex problems with a combination of data, analysis, leadership, and creativity. Currently, I lead a team of data scientists, tech leads, and software developers in building an enterprise machine learning operations platform for Ford Motor Company.

My expertise lies in leveraging data and machine learning, along with innovative ideas, to solve business challenges. I’ve successfully managed every aspect of the process, from evaluating customer needs to informing product development to leading cross-functional teams to success.

My passion for developing teams, combined with my entrepreneurial drive and expertise in data and technology, make me the ideal candidate for any company looking to solve complex challenges and achieve growth.

Valamir – Valmir Bucaj is a technical leader and product owner of Ford’s AI/ML Platform. He has 3+ years of experienced in leading cross-functional teams, who specialize in building multi-cloud enterprise MLOps frameworks to help ML Engineers and Data Scientists scale and productionize their machine learning projects faster and more efficiently. Valmir is passionate in building AI/ML products that customers both need and love. He previously used to work as a machine learning engineer, focusing on graph neural networks for social recommendations.

Valmir used to also work as an assistant professor of mathematics at West Point, where among other things, he designed and taught the Academy’s first Data Science course for their newly established major.

Valmir holds a PhD in Mathematics, from Rice University.

Talk Track: Business Strategy

Talk Technical Level: 3/7

Talk Abstract:
In this talk we will discuss Ford’s journey in building an AI/ML platform from inception to present day. Specifically, we will share the many lessons learned around starting a platform within an organization with deeply rooted traditions. This includes making decisions for product strategy around customer persona, technical stack, buy vs. build, on-prem vs. cloud, and centralized vs. decentralized. We will discuss the reasons behind our evolving product strategy, the challenges in finding the right product-market fit, and the many organizational challenges.

What You’ll learn:
The audience will learn how to navigate organizational challenges in starting a AI/ML ecosystem at a large company with deeply rooted traditions. This will include pitfalls to avoid, building for your customer, and navigating team topology that works best for your ecosystem.

Talk: AI Alignment with Business Goals and Impact (Technical and Strategic Considerations)

Presenters:
Surbhi Rathore, CEO & Co-Founder, Symbl.ai | Kamelia Aryafar, Head of AI Education, Google Cloud AI | Shingai Manjengwa, Head of AI Education, ChainML | Manas Bhuyan, AI & Data Leader, Retail & Consumer Products Practice Data Leader, Deloitte Consulting LLP | Laila Paszti, Co-Founder & CEO, Kirkland & Ellis LLP

About the Speakers:
Surbhi is the CEO and Co-founder of Symbl.ai, building state of the art understanding and generative AI for communication data. As a software developer turned CEO, she is on a mission to deliver exceptional and easy builder-first experience to create distinct product experiences that redefines how machines can elevate human interactions at scale. She’s a strong advocate for immigrant founders and firmly believes in creating value through collaboration without borders.

Kamelia Aryafar is a Senior Engineering Director in Google Cloud AI overseeing teams building Generative Search and Recommendations AI Solutions. Prior to Google, Kamelia was the Executive Vice President, Chief Algorithms and Analytics Officer, and board member for Overstock.com, where she led the company’s. Engineering, analytics, data platform, machine learning, data science and algorithmic products. She also worked on product teams at Etsy. Kamelia holds a Ph.D. and M.Sc. in computer science and ML from Drexel University.-Shingai is the Head of AI Education at ChainML where she works with clients on AI education, adoption, and implementation.

Shingai takes clients from an AI product idea to an affordable and scalable deployment. A seasoned data scientist, Shingai has a background in retail analytics, generating data insights to top tier manufacturers and retailers, and she has led technical education at the Vector Institute for AI. Shingai is also the founder of Fireside Analytics Inc., an online data science education company. Over half a million learners have taken her courses on platforms such as IBM and Coursera. Shingai sits on a number of boards and advisory committees, and she is an award-winning speaker and children’s book author.

Manas is a distinguished AI & Data leader in Deloitte’s Strategy & Analytics practice, specialized in accelerating digital transformation journey for global organizations in Retail and Consumer Products sector. With over 18 years of diverse experience in technology, product management, and consulting, he is a recognized thought leader and trusted advisor to executive teams. Manas aids his business partners in Data & Platform modernization, and unlock business value leveraging Data, Cloud, IoT, AI/ML, and GenAI technologies.

An MBA from Cranfield School of Management, he has authored articles and frequently speaks at industry events on Data, Cloud, AI/ML, & GenAI.

A machine learning engineer turned lawyer, Laila Paszti is a technology & IP transactions partner in the Bay Area office of Kirkland & Ellis LLP. Laila leverages her significant experience in technology, intellectual property and privacy to help clients navigate risk and protect their commercial interests, including when using and developing AI systems.

Laila’s practice focuses on technology and related aspects of mergers and acquisitions, corporate finance, licensing and other commercial transactions. She has acted on both sides of multinational mergers and acquisitions, patent purchases and sales, and licensing and IP transactions. Laila routinely advises clients on evolving multijurisdictional privacy regimes, cybersecurity, big data platform diligence and IP matters. She is sought out for her guidance on artificial intelligence and commercialization, including on patent strategy.
Before law school, she held positions as a software applications engineer at ExxonMobil and Capstone Technology where she designed and implemented neural-network based control systems to optimize industrial processes. Laila holds a B.A.Sc. in Chemical Engineering from the University of Toronto, a M.A.Sc. in Chemical Engineering from the University of Waterloo and a J.D. from the University of Toronto, where she was a law review editor. She is admitted to practice law in California, New York, and Ontario (Canada). Laila is a Certified Information Privacy Professional (Europe and Canada) (CIPP/E and CIPP/C).

Ron Bodkin is the co-founder and CEO of ChainML, which provides an open-source platform for rapidly developing customized generative AI applications using collaborating ‘agents’. The platform enables the robust deployment and monitoring of generative AI models ensuring they can be operated with confidence and accuracy. He has over 15 years of AI experience, specializing in data science, analytics, machine learning, and large scale data processing. Formerly, he worked in Google’s Cloud CTO office, with a focus on Applied Artificial Intelligence leading efforts in industry applications and responsible AI. He also was the VP and GM of AI at Teradata, following Teradata’s acquisition of his company, Think Big Analytics, and led AI Engineering & CIO at Vector Institute.

Talk Track: Business Strategy

Talk Technical Level: 5/7

Talk Abstract:
TBD

Prerequisite Knowledge:
TBD

Talk: Hyper-Scaling Real-Time Personalization with Privacy via On-Device Computing

Presenter:
Varun Kumar Khare, CEO, NimbleEdge

About the Speaker:
Varun Khare, a distinguished alumnus of IIT Kanpur with a Bachelor’s in Technology in Computer Science, has a rich research background that spans across marquee institutions.

He has conducted research at institutions such as the University of California, Berkeley, and at MPI for Brain Research in Germany where he worked on neural symbolic hybrids for program synthesis and built the first petabyte scale 3D segmentation for mapping brain tissues.

Varun has also lead Federated Learning capabilities at Open-Mined and played a pivotal role in driving the open-source movement for secure and private Machine Learning. Notably, his achievements there included pioneering the world’s first open-source platform for privacy-aware Machine Learning on devices at OpenMined, in a joint venture with Facebook (Meta)..

Merging his passion for Federated Learning with his enthusiasm for entrepreneurship and research, Varun founded NimbleEdge with the goal of revolutionizing the technological experiences of billions of users. As the Co-founder and CEO of NimbleEdge, he currently leads the organization in providing a platform that enables companies to leverage edge computing to create an on-device ML ecosystem, delivering hyper-personalized mobile app experiences at an incredible scale.

Talk Track: Workshop

Talk Technical Level: 4/7

Talk Abstract:
The talk focusses on overcoming the de-facto trade-offs on privacy to personalization, latency to throughputs and scale to infrastructure costs. How over-reliance on cloud infrastructure has been a challenge for major applications hindering their revenue streams, complicated deployment cycles and session-aware ML capabilities. The talk will also be exploring the current state of edge computing infrastructure and the path ahead for MLOps.

What You’ll Learn
Our experiences have shown ML teams to save over 50% of their cloud infra costs (amounting to $10-20M savings per year). The world still thinks of edge as limited to IoT while in reality, edge, especially with privacy, is a phenomenon for mobile and desktop apps. Given the proliferation of the edge computing, MLOps community can extract a lot of under-utilized compute capabilities on users’ devices. What everyone thinks of as default trade-offs do not exist anymore and we as data scientists have technologies to build great user experiences without being intrusive.

Workshop: Production ML Serving & Monitoring in Kubernetes

Presenter:
Andrew Willson, Head of Customer Success, Seldon

About the Speakers:
Andrew is the Head of Customer Success at Seldon, enabling enterprise customers to get the most out of the Seldon ecosystem of tools. Andrew has spent the last 10 years in technical, client-facing roles with a focus on data, machine learning, and architecture. He loves transforming complex and ambiguous topics into clear actions that drive value within an organization. Outside of work, you might find Andrew riding his bike around the streets of London or snowboarding somewhere in The Alps.

Talk Track: Workshop

Talk Technical Level: 5/7

Talk Abstract:
This talk offers a practical guide to building a state-of-the-art MLOps deployment platform using Kubernetes, with a focus on deploying deep learning models. Attendees will gain insights into the integration of key technologies including NVIDIA Triton Inference Server, Seldon Core v2, Kafka, Prometheus, and Grafana. The session covers an end-to-end workflow for serving complex models like transformers and CNNs and configuring monitoring on top. The knowledge shared will be valuable for those looking to enhance model performance, reduce costs, and unlock new use cases in machine learning.

What You’ll Learn:
“
By attending this talk, the audience will learn how to set up a state-of-the art MLOps deployment platform on top of Kubernetes. Once it is up and running, they will learn how to serve deep learning models in a robust and scalable way. As inference is being performed, the audience will see how they can configure rich monitoring to observe their models in production.

Along the way, the audience will learn about each of the technologies being presented, including: Kubernetes, PyTorch, NVIDIA Triton Inference Server, Seldon Core v2, Kafka, Prometheus, and Grafana. More importantly, they will see how these tools integrate with one another to form a fully fledged MLOps deployment platform.

MLOps deployment platforms are very challenging systems to build, deploy, and operate in the real-world. As more and more business value is being driven by large, complex, deep learning (e.g. transformer, diffusion, CNN) models, it is crucial to be able to serve these in a scalable way. This type of a platform creates business value by: (1) reducing inference costs through efficient serving, (2) improving performance through continuous monitoring and deployment, (3) enhancing the user experience with low latency, (4) shortening the time to bring use cases to market, and (5) eliminating the up-front development costs of building a deployment platform.

Aside from benefitting the business, engineers can benefit knowing that they are using best-in-breed tools, that they have a consistent way of deploying ML models, and that they have monitoring and logging built in. Enabling engineers to build complex, data-centric pipelines opens up a new set of use cases, including advanced recommendation engines, explainable machine vision models, and even LLM question-and-answer applications.

Workshop: LLMs in Practice: A Guide to Recent Techniques and Trends

Presenters:
Ville Tuulos, CEO, Outerbounds | Eddie Mattia, Data Scientist, Outerbounds

About the Speakers:
Ville has been developing infrastructure for machine learning for over two decades. He has worked as an ML researcher in academia and as a leader at a number of companies, including Netflix where he led the ML infrastructure team that created Metaflow, a popular open-source framework for data science infrastructure. He is a co-founder and CEO of Outerbounds, a company developing modern human-centric ML. He is also the author of the book, Effective Data Science Infrastructure, published by Manning.

Eddie Mattia is a data scientist at Outerbounds who began using Python to teach applied math in grad school. Since then, Eddie has worked at startups and at Intel building machine learning software

Talk Track: Workshop

Talk Technical Level: 5/7

Talk Abstract:
In this workshop, attendees will learn about methods for working with LLMs. Our stories will be guided by examples you can run on your laptop or in a (free) hosted cloud environment provided to attendees. Developers will expand their awareness of how researchers and product designers are working with LLMs, with emphasis on connecting high-level concepts such as fine-tuning and vector databases to the fundamental math and APIs data scientists should understand. Business-minded executives can either get hands – on or follow the higher-level stories to deepen their sense of what is possible with LLMs, the technicalities behind risks they introduce, and how they fit into the arc of ML. The primary value of this workshop will be as a guide to help teams set reasonable goals in the complex and fast-moving world of LLMs, and understand what you need to successfully support your team’s next LLM projects.

What You’ll Learn:
There are cheap (e.g., APIs) and expensive (e.g., fine-tuning, training) ways to build on top of LLMs. The methods you choose have consequences in apps you can build and how your dev team works. We will learn how to think about these choices as we develop basic apps you can use as templates for future genAI projects. Learners have the option to follow along in a provided dev environment where we will unpack these choices and make the tradeoffs and decision space concrete.

Prerequisite Knowledge:
– Basic knowledge of Python
– Ability to use the command line
– Ability to use common ML algorithms in a notebook environment

Workshop: Introduction to Building ML Microservices: A Hands-On Approach with Examples from The Music Industry

Presenter:
Ramon Perez, Developer Advocate, Seldon

About the Speaker:
Ramon is a data scientist and educator currently working in the Developer Relations team at Seldon in London. Prior to joining Seldon, he worked as a freelance data professional and as a Senior Product Developer at Decoded, where he created custom data science tools, workshops, and training programs for clients in various industries. Before Decoded, Ramon wore different research hats in the areas of entrepreneurship, strategy, consumer behavior, and development economics in industry and academia. Outside of work, he enjoys giving talks and technical workshops and has participated in several conferences and meetup events.

Talk Track: Workshop

Talk Technical Level: 3/7

Talk Abstract:
Serving a machine learning model is not particularly easy, especially if we add two or three models in parallel to the mix, in which case, a single model deployment recipe might start to crumble. To tackle the challenges around serving individual or multiple models in production, we have handy tools like MLServer and Seldon Core. The former is a python library that allows us create machine learning microservices with one or multiple models in the same service, and the latter allows us to build simple-to-complex inference graphs that can help us handle A/B testing, shadow and canary deployment, feature transformations, and model monitoring. If you want to learn how to use open-source tools to build microservices based on your different use cases and model recipes, come and join this hands-on workshop and get started with several of the key steps in the machine learning workflow as we walk through fun examples from the broader music industry.

What You’ll Learn:
The core of the workshop will teach participants how to create machine learning microservices and inference graphs, and how to monitor the predictions made by these services. The main use case we’ll follow throughout the workshop comes from the music industry, so this will be a fun and content-rich 3 hours to go through.

Throughout the workshop, we will be building a creative ML platform in several incremental steps. In the first 50 minutes of the workshop, we will set up the user interface and the back-end our application, and then we’ll spin up the first model we will interact with. In the second 50-minute section, we will start adding different functionalities to our platform by running new machine learning models inside our inference server. Lastly, we’ll create different replicas of each model, develop an inference graph to come up with unique tunes, and conduct AB testing on our service to assess and evaluate the output of different models when compared with real songs.

Within our 3 hours together, we’ll have two 10- to 15-minute breaks and there will be plenty of exercises for participants to complete.

Workshop: Avoid ML OOps with MLOps: A Modular Approach to Scaling Forethought’s E2 E Ml Platform

Presenter:
Salina Wu, Senior Machine Learning Infrastructure Engineer, Forethought

About the Speaker:
Salina Wu is a Sr. Machine Learning Infrastructure engineer at Forethought.ai. She works closely with the Machine Learning team to build and maintain their end-to-end training, serving, and data infrastructures. She is particularly motivated by introducing new ways to improve efficiency and reduce cost across the ML space. When not at work, Salina enjoys surfing, pottery, and being in nature.

Talk Track: Workshop

Talk Technical Level: 5/7

Talk Abstract:
As Machine Learning becomes more ubiquitous in business and product applications, the need for a cost-efficient, scalable, and automated infrastructure to support the end-to-end ML lifecyle becomes mission critical. However, a scalable and reusable ML Ops platform is often an afterthought in productionizing ML models, due to urgency of business needs and lack of resources or experience. A very common scenario is for ML Ops to be ad-hoc and de-centralized, with no good way to reproduce or automate ML processes. It can be challenging, especially for smaller teams, to identify and foresee specific ML Ops needs and understand how to address them.

Forethought is an enterprise company building AI-powered customer experience (CX) solutions. Our products require training customer-specific language models and deploying them on low-latency, high-uptime endpoints. With ML at the heart of our business, our infrastructure supporting it is pivotal to our growth and success. At Forethought, we took a close look at our initial ML infrastructure, aiming to identify key areas of improvement and anticipate future requirements. Through a step-by-step approach, we gradually replaced our existing infrastructure with improved, modular components to arrive at a much more mature system. This case study will dive into which areas we identified as critical to replace as well as the steps we took to enhance them. In particular, we will look at the following:

– Streamlining ML training and migrating to the Sagemaker training platform
– Achieving efficient model serving with Sagemaker Serverless and Multi-Model Endpoints
– Orchestrating our ML processes with automated pipelines on Dagster
– Centralizing ML feature engineering across our datalake using Spark
– Building intuitive model management tooling with Retool

What You’ll Learn:
– Understanding the different components of a solid ML infrastructure
– Identifying and proactively addressing bottlenecks and opportunities for growth in your ML lifecycle
– Learning how to improve and migrate your ML infrastructure in stages
– Understanding the goals and best practices of a stable end-to-end ML infrastructure

Talk: Wanted: A Silver-Bullet MLOps Solution for Enterprise - Learnings from Implementing Sustainable MLOps in Pharma Research

Presenter:
Muller Mu, Solution Architect / Senior Scientist, Roche & Scientific | Christophe Chabbert, MLOps Service & Product Manager, Roche

About the Speaker:
Having amassed extensive experience in digitalization across different industries, Le (Muller) Mu is combining his IT experience with his knowledge in molecular biology to help accelerate the drug discovery process in Roche Pharma Research and Early Development (pRED). He is currently the tech lead on the Roche pRED MLOps Service team, driving the operationalization of many different machine learning models, which helps to bring better future medicines faster into the hands of the patients.

Originally trained as a bioinformatics scientist, Christophe now has over a decade of experience at the intersection of data science, machine learning, and drug discovery. Currently serving as a Service & Product Manager for Machine Learning Operations at Roche, Christophe is dedicated to operationalizing machine learning at scale, enabling better decision-making in the drug discovery pipeline. He is very passionate about the transformative potential of AI and ML in health care. Prior to his role at Roche, Christophe also held key positions in the pharmaceutical and bioinformatics fields, demonstrating a commitment to advancing the frontiers of advanced analytics in life sciences.

Talk Track: Business Strategy

Talk Technical Level: 3/7

Talk Abstract:
The pharmaceutical research and early development (pRED) business unit in Roche is facing an increasing demand for operationalization of Machine Learning models that are making critical impacts on its value chain. While this demand is putting pressure on IT departments to deliver end-to-end and scalable MLOps solutions, readily-available tools are still maturing. In particular, the fragmentation and volatility of the current landscape makes the selection of such tools and their maintenance a daunting challenge for many teams. In this talk, we would like to present key learnings and recommendations from our 4-year MLOps journey. We will present and discuss how we sustainably served a broad spectrum of users and use cases with diverse requirements, technological literacy and data modalities. Finally, we would also like to share some perspectives for the future of MLOps in our research organization.

What You’ll learn:
1. The audience will learn the diverse requirements and unique challenges in implementing MLOps in Pharma Research
2. The audience will learn how we have built business and technical solutions in addressing these challenges so far
3. The audience will learn how we have identified the gap between readily-available MLOps platforms/solutions (commercial or open-source) and the needs for successfully implementing sustainable MLOps in an enterprise context with diverse requirements (such as in Pharma Research)
4. The audience will learn how we think this gap can be bridged by adhering to some of the fundamental principles/concepts in software engineering.

Talk: LLM Economics : The Cost of Leveraging Large Language Models

Presenter:
Nikunj Bajaj, CEO & Co-Founder, TrueFoundry

About the Speaker:
Nikunj is the co-founder and CEO of TrueFoundry, a platform empowering ML developers to deploy and optimize Language Models. Prior to this role, he served as a Tech Lead for Conversational AI at Meta, where he spearheaded the development of proactive virtual assistants. His team also put Meta’s first deep learning model on-device. Nikunj also led the Machine Learning team at Reflektion, where he built an AI platform to enhance search and recommendations for over 600 million users across numerous eCommerce websites. Fun Fact about Nikunj? He learnt scuba diving and swimming in parallel. His instructor laughed at him saying- “You don’t know how to swim? And you thought it would be just fine to jump in the middle of Pacific with 70 lb gear on your back

Talk Track: Business Strategy

Talk Technical Level: 3/7

Talk Abstract:
Most of us are using LLMs and some of us are getting to the point where LLMs are going to production. Honeymoon phase is going to get over soon and practical realities like cost & maintainability are going to become mainstream.
However, the cost of running LLMs is not well understood or often not put in perspective. In this talk we will dive deep into what type of costs are involved in building LLM based apps. How do these compare when you run RAG vs Fine tuning, what happens when you use Open Source vs Commercial LLMs?
Spoiler- If you wanted to summarize the entire Wikipedia to half its size using GPT-4 8k context window, it would cost a whopping $360K!

What You’ll learn:
The highlighted takeaways from our session provide valuable insights specific to LLM pricing. By understanding the economics of different models, businesses and LLM users can make informed decisions tailored to their needs. Comparing costs across models, such as GPT-4, Anthropic Claude V1, InstructGPT-DaVinci, Curie, and self-hosted 7B models, reveals the significant variations in pricing. This knowledge enables organizations to evaluate the trade-off between cost and performance effectively, ensuring optimal resource allocation.

Additionally, we’ll provide the breakdown of pricing levers, including input and output costs based on the number of tokens, which allows businesses or individuals to analyze their usage patterns and manage expenses efficiently. Understanding the cost implications of fine-tuning models empowers organizations to make strategic decisions based on their specific requirements and budget.

The introduction of TrueFoundry’s solutions, such as the compression API for reducing OpenAI costs and simplified deployment of open-source LLMs, offers tangible strategies for optimizing expenses. These unique approaches provide actionable steps that businesses can implement to reduce LLM-related costs while maintaining desired performance levels.

Talk: From Data Collection to ML Model Deployment in Less than 15 Minutes

Presenter:
Hudson Buzby, Solutions Architect, Qwak

About the Speaker:
Hudson Buzby is a dynamic Solutions Architect with a passion for leveraging technology to solve complex challenges. With over a decade of experience in the tech industry, Hudson has made significant contributions to the world of data engineering and cloud solutions. Hudson currently serves as a Solutions Architect at Qwak, where he continues to push the boundaries of what’s possible in the world of cloud computing and data management.

Talk Abstract:
The Qwak MLOps Platform is a comprehensive platform tailored to empower data scientists, engineers, and organizations, facilitating the acceleration of their machine learning (ML) endeavors with unmatched simplicity and effectiveness. During this demonstration, we will present the platform’s pivotal attributes and functionalities:

Effortless Model Deployment: Qwak MLOps streamlines the intricate task of deploying ML models into production environments. With a few straightforward clicks, users can seamlessly deploy models across various cloud or on-premises setups, eradicating the intricacies associated with deployment pipelines.

Automated Monitoring and Scalability: The platform offers robust monitoring and scaling capabilities. It constantly evaluates model performance and autonomously adjusts resources, ensuring optimal efficiency and minimizing downtime.

Version Control and Collaborative Tools: Collaborative model development becomes a breeze through integrated version control and collaboration tools. Data scientists and engineers can collaborate seamlessly, promoting transparency and reproducibility in ML projects.

Comprehensive Model Lifecycle Management: Qwak MLOps delivers end-to-end lifecycle management, spanning data ingestion, preprocessing, model training, and inference. This holistic approach streamlines the entire ML pipeline, enhancing productivity and expediting time-to-market.

Intuitive User Interface: The user-friendly interface of the Qwak MLOps Platform ensures that both technical and non-technical stakeholders can effortlessly navigate and leverage its capabilities, fostering cross-functional collaboration.

We invite you to participate in our live Qwak MLOps Platform demonstration, where you can witness firsthand how it transforms ML operations, making model deployment and management more accessible and efficient than ever before. Explore how Qwak empowers your organization to leverage the potential of machine learning, propelling data-driven success in today’s competitive landscape.

Talk: Navigating the Nuances of AI Insights Management & Governance in a Dynamic Business Environment

Presenter:
Ambal Balakrishna, Executive Director of Strategic Initiatives, Cisco | Ilker Yaramis, Senior Data Scientist, Kaiser Permanente

About the Speaker:
Ambal Balakrishnan is currently Executive Director of Strategic Initiatives at Cisco where she leads transformation and governance for Cisco’s large portfolio of offerings. She has spent over a couple of decades in the Corporate world in various roles that includes Engineering, Product Management, Business development, Strategy and Marketing for premium and fast growing product divisions at Cisc, Dell, IBM. Prior to her current role in Cisco she led Dell’s Go to Market for Dell’s aaS portfolio. Prior to her Dell role she fostered aaS growth for IBM Data & AI through the adoption of customers success metrics via OKRS methodology.

Ambal’s book is #CONTENTMARKETINGtweet – 140 bite-sized ideas to create and market compelling content. She holds 2 patents – one on Secure data display and another on Image dispositioning using machine learning.

Ambal is an avid reader and hiker. Her hiking pursuits have taken her to several mountains including Mt. Whitney at 14,500 feet (which she managed to climb in 1 day).

Ambal received her Masters in Computer Science from Purdue University and an MBA in Marketing, Strategy and Entrepreneurship from Wharton University of Pennsylvania. Her ongoing education includes Artificial Intelligence: Implications for Business Strategy from MIT and Executive Women in Leadership from Cornell.

She lives in Austin, TX with her family and a border-collie+lab mix dog named Rainbow.

Ilker holds the role of a Staff Data Scientist at Kaiser Permanente, primarily focused on the development of machine learning solutions and leadership in digital twin initiatives. Prior to his tenure at Kaiser Permanente, he contributed his expertise as a data scientist at Apple. His educational journey includes the attainment of a master’s degree in Computer Science from the Georgia Institute of Technology. Presently, he is dedicated to pursuing a Ph.D. in computer science with a specialization in healthcare at the University of Washington.

During his leisure hours, Ilker finds joy in spending quality time with his daughters and wife. His recreational activities encompass camping, hiking, swimming, and soccer.

Talk Abstract:
In an age where data-centric decisions are paramount, AI insights and governance have become fundamental for thriving business operations. The upcoming expert discussion, titled “Navigating the Nuances of AI Insights Management in a Dynamic Business Environment,” will explore strategies for AI governance, addressing challenges, ensuring compliance, and embracing future technological trends. This will be a great opportunity to learn from the experiences of these seasoned veterans.

Meet Our Speakers

Advanced Technical / Research

Business Strategy

Case Studies

In-Person Workshops

Panel Discussion

Virtual Talks and Workshops

Lightning Talks

Speakers Corner

Future of AI

Agenda

Tickets

Talk: MLOps on Highly Sensitive Data - Strict Confinement, Confidential Computing and Tokenization Protecting Privacy

Talk: Operationalizing Data-Centric Ai: Practical Algorithms + Software to Quickly Improve ML Datasets

Talk: MLOps for Graph-Based Recommender Systems: Orchestrating Intelligent Connections

Talk: End-to-end Production Machine Learning Workflows

Talk: How many Labelled Examples Do You Need for a BERT-sized Model to Beat GPT4 on Predictive Tasks?

Talk: Gen AI and Lightning: Accelerating AI Innovation while Ensuring Scalability and Security

Talk: Evaluation Techniques for Large Language Models

Talk: Your AI Applications Need Guardrails: Here's How To Build Them

Talk: Stable Diffusion for Your Images: Custom Dream

Talk: Using Scouter Models to Monitor Model Drift - A Novel Approach

Talk: Is It Too Much to Ask for A Stable Baseline?

Talk: Introduction to LangChain and Retrieval Augmented Generation (RAG)

Talk: Data Versioning in Generative AI: A Pathway to Cost-Effective ML

Talk: Lessons Learned from Implementing GenAI at Large Enterprises

Talk: From Analytics to AI How Gen AI Can Unlock Data Insights and Transform Decision-Making

Talk: Unlocking Potential: A Fireside Chat on Generative AI, Reasoning, and Logic in Enterprise Innovation

Talk: LLMOps: An Emerging Stack to Productionalize LLM Applications

Talk: Beyond the Kaggle Paradigm: Future of End-to-End ML Platforms

Talk: Building Computers for AI

Talk: Tales of Innovation Within a 100 Year Old Company

Talk: Removing the Roadblocks to Build Great GenAI Products

Talk: Getting Higher ROI on MLOps Initiatives: Five Lessons Learned While Building out The MLOps Platform for 100+ Data Scientists

Talk: AI Alignment with Business Goals and Impact (Technical and Strategic Considerations)

Talk: Hyper-Scaling Real-Time Personalization with Privacy via On-Device Computing

Talk: Cracking the Code: Why SWEs Should Embrace Prompt Engineering (and What They Risk by Sticking to Their Comfort Zone)

Talk: Training Large Language Models: Lessons from The Trenches

Talk: Supercharging Search with LLMs: The Instacart Journey

Talk: Evolved Structures: Using AI and Robots to Build Spaceflight Structures at NASA

Talk: Low-latency Model Inference in Finance

Talk: Supercharging Recommender Systems: Unleashing the Power of Distributed Model Training

Talk: Evolution of ML Training and Serving Infrastructure at Pinterest

Talk: Amumu Brain; How League of Legends Uses Machine Learning an Applied Data Science

Talk: Fine-Tune LLMs or Integrate 3rd party APIs? A Financial Case-Study

Talk: Creating the World’s Premier Biological Foundation Model

Talk: From Prototype to Product: Rapid iteration and ML Model Deployment at Dropbox

Workshop: Build Your Own ChatGPT with Open Source Tooling

Workshop: Retrieval Augmented Generation (RAG) with LangChain: “ChatGPT for Your Data” with Open-Source Tool

Workshop: Chat with MLOpsWorld: Engineering an LLM Application

Workshop: MLOps for Production-Ready LLM — Putting LLMs into Production Through Iterative Training, Fine-Tuning, and Serving

Workshop: Production ML Serving & Monitoring in Kubernetes

Talk: How to Finetune Your LLMs and Evaluate Performance

Workshop: Deploying Generative AI Models: Best Practices and An Interactive Example

Workshop: LLMs in Practice: A Guide to Recent Techniques and Trends

Workshop: Applying GitOps Principles at Every Step of An E2E MLOps Project - An Interactive Workshop

Workshop: Learn Your Codebase: Fine-tuning CodeLlama with Flyte… to Learn Flyte

Workshop: Feature Stores in Practice: Train and Deploy an End-to-End Fraud Detection Model with Featureform, Redis, and AWS

Workshop: Applying Responsible AI with the Open-Source LangTest Library

Workshop: Introduction to Building ML Microservices: A Hands-On Approach with Examples from The Music Industry

Workshop: Finetuning a Large Language Model on A Custom Dataset

Workshop: Avoid ML OOps with MLOps: A Modular Approach to Scaling Forethought’s E2 E Ml Platform

Workshop: Lessons Learned: The Journey to Real-Time Machine Learning at Instacart

Workshop: QA in ML

Talk: Wanted: A Silver-Bullet MLOps Solution for Enterprise - Learnings from Implementing Sustainable MLOps in Pharma Research

Workshop: Learning from Extremes: What Fraud-Fighting at Scale Can Teach Us About MLOps Across Domains

Workshop: How to Design and Build Resilient Machine Learning Systems

Workshop: Spend Less Time Troubleshooting ML Production Issues

Workshop: What Does It Mean to Be an "AI Engineer" in The World of Generative AI. Merging Business Strategy and The Technical Ecosystem

Workshop: Ingredients of a Comprehensive MLOps Program at Chick-fil-A

Talk: Making ChatGPT funny with Prompt Optimization

Talk: LLM Economics : The Cost of Leveraging Large Language Models

Workshop: Building a Conversation-focused LLM on Communication Data

Workshop: Lessons Learned Productionising LLMs for Stripe Support

Talk: How Booking.Com Is Approaching LLMOps: Approaches & Considerations, Pro's & Con's

Talk: LLM Evaluation to Craft Delightful Content from Messy Inputs

Talk: Intelligent Writing Assistants in the era of Large Language Models

Talk: LLMs from Hallucinations to Relevant Responses

Talk: Building an End-To-End MlLOps Pipeline

Talk: Generative AI: The Open Source Way