Join our free GenAI Tools, Infra & Open Source Event

By Toronto Machine Learning Society (TMLS)

4th Annual

Austin TX, October 25th to 26th

Workshops & Use Cases Covering

Organized by

Community Driven Content.

Guided and selected by our committee to ensure the lessons and takeaways across these two days will help you put more models into production environments, effectively, responsibly, and efficiently.

Co-located alongside

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Renaissance Austin Hotel 1
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RESOURCES

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EXPERIENTIAL NETWORKING

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Where

Renaissance Austin Hotel
9721 Arboretum Blvd
Austin, TX 78759, USA

When

Wednesday, October 25, 2023 - 9:00 AM Thursday, October 26, 2023 - 6:00 PM MDT

10+ Workshops Topics

Rajiv-Shah
Rajiv Shah, Machine Learning Engineer, Hugging Face
Anouk Dutree, Product Owner, UbiOps

• Scaling & Deployment • MLOps Challenges • Open Source Integration • Privacy and Security • Operational Efficiency

Strategy

Dr. Ilyas Iyoob
Dr. Ilyas Iyoob, Faculty, University of Texas
Reuven Cohen, CTO Generative AI, EY Americas

Technical Sessions

Aishwarya Reganti
Aishwarya Reganti, Applied Scientist, Amazon
Hagay Lupesco
Hagay Lupesco, VP Engineering, MosaicML
Hien Luu
Hien Luu, Head of ML Platform, DoorDash
Vincent David
Vincent David, Senior Director - Machine Learning, Capital One
Matthew Honnibal

Matthew Honnibal

Founder & CTO, Explosion AI
Talk: How many Labelled Examples Do You Need for a BERT-sized Model to Beat GPT4 on Predictive Tasks?

Goku Mohandas

Goku Mohandas

ML Lead, Anyscale
Talk: End-to-end Production Machine Learning Workflows

Jonas Mueller

Jonas Mueller

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

William Falcon

William Falcon

Founder & CEO, Lightning AI
Talk: Gen AI and Lightning: Accelerating AI Innovation while Ensuring Scalability and Security

Andreea Munteanu

Andreea Munteanu

AI/ML Product Manager, Canonical
Talk: MLOps on Highly Sensitive Data - Strict Confinement, Confidential Computing and Tokenization Protecting Privacy

20+ Use Cases

Norm Zhou

Norm Zhou

Engineering Manager, Meta
Talk: Beyond the Kaggle Paradigm: Future of End-to-End ML Platforms

Ryan McClelland

Ryan McClelland

Research Engineer, NASA Goddard Space Flight Center
Talk: Evolved Structures: Using AI and Robots to Build Spaceflight Structures at NASA

Prakash Putt

Prakash Putt

Staff Software Engineer, Instacart
Talk: Supercharging Search with LLMs: The Instacart Journey

Aayush Mudgal

Aayush Mudgal

Senior Machine Learning Engineer, Pinterest
Talk: Evolution of ML Training and Serving Infrastructure at Pinterest

David Bennett

David Bennett

Chief Customer Officer, Tenstorrent
Talk: Building Computers for AI

More added daily

Join us at 4th Annual MLOps World

Talk: Evaluation Techniques for Large Language Models

Presenter:
Rajiv Shah, Machine Learning Engineer, Hugging Face

About the Speaker:
Rajiv Shah is a machine learning engineer at Hugging Face who focuses on enabling enterprise teams to succeed with AI. Rajiv is a leading expert in the practical application of AI. Previously, he led data science enablement efforts across hundreds of data scientists at DataRobot. He was also a part of data science teams at Snorkel AI, Caterpillar, and State Farm.

Rajiv is a widely recognized speaker on AI, published over 20 research papers, been cited over 1000 times, and received over 20 patents. His recent work in AI covers topics such as sports analytics, deep learning, and interpretability.

Rajiv holds a PhD in Communications and a Juris Doctor from the University of Illinois at Urbana Champaign. While earning his degrees, he received a fellowship in Digital Government from the John F. Kennedy School of Government at Harvard University. He has recently started making short videos, @rajistics, with several million views.

Talk Track: Advanced Technical/Research

Talk Technical Level: 5/7

Talk Abstract:
Large language models (LLMs) represent an exciting trend in AI, with many new commercial and open-source models released recently. However, selecting the right LLM for your needs has become increasingly complex. This tutorial provides data scientists and machine learning engineers with practical tools and best practices for evaluating and choosing LLMs.

The tutorial will cover the existing research on the capabilities of LLMs versus small traditional ML models. If an LLM is the best solution, the tutorial covers several techniques, including evaluation suites like the EleutherAI Harness, head-to-head competition approaches, and using LLMs for evaluating other LLMs. The tutorial will also touch on subtle factors that affect evaluation, including role of prompts, tokenization, and requirements for factual accuracy. Finally, a discussion of model bias and ethics will be integrated into the working examples.

Attendees will gain an in-depth understanding of LLM evaluation tradeoffs and methods. Jupyter Notebooks will provide reusable code for each technique discussed.

What You’ll Learn:
Ways to quickly start evaluating models

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

Presenter:
Anouk Dutree, Product Owner, UbiOps

About the Speaker:
Anouk is the Product Owner at UbiOps. She studied Nanobiology and Computer Science at the Delft University of Technology, and did a Master’s in Game Development at Falmouth University, which spiked her interest in Machine Learning. Next to her role at UbiOps, she frequently writes for Towards Data Science about various MLOps topics and she co-hosts the biggest Dutch data podcast, de Dataloog. Her efforts in tech have been awarded twice with the T500 award, in both 2020 and 2021

Talk Track: Workshop

Talk Technical Level: 4/7

Talk Abstract:
Generative AI models are all the hype nowadays, but how do you actually deploy them in a scalable way? In this talk we will discuss best practices when moving models to production, as well as show an interactive example of how to deploy one using UbiOps. UbiOps is a serverless and cloud agnostic platform for AI & ML models, built to help data science teams run and scale models in production. We will pay special attention to typical hurdles encountered in deploying (generative) AI models at scale. Python knowledge is all you need for following along!

What You’ll Learn:
Deployment at scale doesn’t have to be difficult. Participants will learn how to deploy a generative AI model to the cloud themselves, and how to select the right hardware for your use case (CPU,GPU,IPU etc.).

Prerequisite Knowledge:
Python knowledge and a basic understanding of AI/ML models

Talk: Lessons Learned from Implementing GenAI at Large Enterprises

Presenter:
Dr. Ilyas Iyoob, Faculty, Faculty at University of Texas, Chief Scientist at Kyndryl

About the Speaker:
Dr. Ilyas Iyoob is chief data scientist and global head of Research at Kyndryl. He pioneered the seamless interaction between machine learning and operations research in the fields of autonomous computing, fintech, and blockchain. As a successful entrepreneur at Gravitant, a start-up focused on optimizing the cloud journey, he helped build and sell the company to IBM in 2016. Dr. Iyoob currently advises over a dozen venture funded companies and serves on the faculty of the Cockrell School of Engineering at the University of Texas at Austin. He has earned a number of patents and industry recognition for cloud intelligence and was awarded the prestigious World Mechanics prize by the University of London.

Talk Track: Business Strategy

Talk Technical Level: 3/7

Talk Abstract:
In the rapidly evolving landscape of GenAI, large US enterprises face unique challenges when considering its implementation. Beyond the well-acknowledged concerns of data privacy, security, bias, and regulatory compliance, our journey in executing GenAI within mission-critical applications has revealed additional complexities. In this session we will walk through a number of real examples of failed implementations and the lessons learned from them.

What You’ll learn:
Learn from mistakes; new best practices for successful implementation of GenAI

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: 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: Training Large Language Models: Lessons from The Trenches

Presenter:
Bandish Shah, Engineering Manager, MosaicML/Databricks

About the Speaker:
Bandish Shah is an Engineering Manager at MosaicML/Databricks, where he focuses on making generative AI training and inference efficient, fast, and accessible by bridging the gap between deep learning, large scale distributed systems and performance computing. Bandish has over a decade of experience building systems for machine learning and enterprise applications. Prior to MosaicML, Bandish held engineering and development roles at SambaNova Systems where he helped develop and ship the first RDU systems from the ground up and Oracle where he worked as an ASIC engineer for SPARC-based enterprise servers.

Talk Track: Case Study

Talk Technical Level: 5/7

Talk Abstract:
Training large AI language models is a challenging task that requires a deep understanding of natural language processing, machine learning, and distributed computing. In this talk, we will go over lessons learned from training models with billions of parameters across hundreds of GPUs. We will discuss the challenges of handling massive amounts of data, designing effective model architectures, optimizing training procedures, and managing computational resources. This talk is suitable for ML researchers, practitioners, and anyone curious about the “sausage making” behind training large language models.

What You’ll learn:
– The challenges in training models with billions of parameters across hundreds of GPUs
– Tips and tricks to make the training work and the model to achieve high accuracy

Talk: LLMOps: An Emerging Stack to Productionalize LLM Applications

Presenter:
Hien Luu, Head of ML Platform, DoorDash

About the Speaker:
Passionate about the intersection of big data & machine learning. Extensive working experience in designing and building big data applications and scalable web-based applications. Have 3 plus years of technical leadership experience in managing multiple data infrastructure related projects. Passion for architecting scalable and highly available big data applications and systems.

Specialties: Big data, web application framework, cloud computing, RESTful web services and cryptography.

Part-time Passion:
* Instructor at UCSC Extension: Apache Spark, Apache Hadoop, Spring Framework, Design Patterns and Java Comprehensive.

Speaking Engagements:
* QCon NY 2019 – track host for ML for Developers track
* QCon (SF, Shanghai, London, AI)
– Building Recommender Systems w/ Apache Spark 2.x
– Recommender System: Powering LinkedIn Growth & Engagement
– Apache Spark 2.x Workshop
* Seattle Data Day – Extreme Streaming Processing at Uber
* 2015 – ArchSummit – Big Data Story : from an engineer’s perspective
* 2013 – Lucene/Solr Revolution – How Lucene Powers the LinkedIn Segmentation and Targeting Platform
* 2013 – Hadoop Summit – LinkedIn Segmentation & Targeting Platform: A Big Data Application
* 2010 – JavaOne – REST with Spring Framework and JAX-RS

Talk Track: Business Strategy

Talk Technical Level: 3/7

Talk Abstract:
TBA

What You’ll learn:
The unique challenges that LLM applications bring and an emerging LLMOps stack can help w/ those challenges to support and operationalize LLM applications.

Talk: Low-latency Model Inference in Finance

Presenters:
Vincent David, Senior Director – Machine Learning, Capital One | Michael Meredith, Lead Software Engineer, Capital One

About the Speakers:
Experienced Machine Learning & Engineering leader with a history of working in Fintech and Entertainment. Strong data and quantitative background, with deep knowledge of complex systems and experiences working in multiple industries. Skilled in Machine Learning, Cloud Engineering. Passionate about using technology to solve high-value business problems.

Talk Track: Case Study

Talk Technical Level: 5/7

Talk Abstract:
Model Inference at Capital One as across the Fintech sector is a key aspect of the Model Development Lifecycle. In order to reap the benefits of machine learning models trained by Data Scientists, we are required to deploy said models in a production environment. This enables critical business applications, such as credit decisions or fraud detection. Increasingly, we are faced with demanding non-functional requirements for high resilience and low latency service response leading us to invest into service oriented architectures for model inference. Seldon has emerged over the last years a key solution to address these challenges.

In this presentation we will take a close look at the recently released Seldon V2 and our findings for its application for financial services at enterprise scale, comparing it to its V1 specification. We will summarize our findings as they relate to the benefits of novel improvements as well as challenges we see in establishing much needed controls to establish this new release in a highly regulated environment.

What You’ll learn:
Practical learnings, insights, and considerations for more effectively deploying models in a production environment – especially complex production environments.

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: End-to-end Production Machine Learning Workflows

Presenter:
Goku Mohandas, ML Lead, Anyscale

About the Speaker:
TBA

Talk Track: Advanced Technical/Research

Talk Technical Level: 4/7

Talk Abstract:
We’ll start by breaking down the machine learning development lifecycle into experimentation (design + develop) and production (deploy + iterate). We’ll walk through the best practices for developing and executing ML workloads and iteratively build a production workflow around it (manual to CI/CD to continual learning). We’ll also take a look at the biggest obstacles in the way of taking machine learning in production (standardization, integrations, scaling workloads in Python, dev to prod transition, reliability, etc.) While our specific use case will be fine-tuning an LLM for a supervised NLP use case, all the content will easily extend to any algorithm (regression to LLMs), application (NLP, CV, tabular, etc.), tooling stacks and scales.

What You’ll Learn:
– Learn the best practices for developing ML workloads (data, train, tune, serve, etc.)
– Learn how to incorporate MLOps concepts to our data science work (experiment tracking, testing code, data + models, monitoring, etc.)
– Learn how to fine-tune an LLM for a supervised use case
– Learn how to iteratively put ML into production (manual deployment, CI/CD, continual learning)
– Learn how to execute ML workloads across multiple machines easily

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

Presenter:
Jonas Mueller, Chief Scientist, Cleanlab

About the Speaker:
Jonas Mueller is Chief Scientist and Co-Founder at Cleanlab, a software company providing data-centric AI tools to efficiently improve ML datasets. Previously, he was a senior scientist at Amazon Web Services developing AutoML and Deep Learning algorithms which now power ML applications at hundreds of the world’s largest companies. In 2018, he completed his PhD in Machine Learning at MIT, also doing research in NLP, Statistics, and Computational Biology. Jonas has published over 30 papers in top ML and Data Science venues (NeurIPS, ICML, ICLR, AAAI, JASA, Annals of Statistics, etc). This research has been featured in Wired, VentureBeat, Technology Review, World Economic Forum, and other media. He loves contributing to open-source, and helped create the fastest-growing open-source software for AutoML  and Data-Centric AI. An avid educator, he also taught the first-ever course on data-centric AI at MIT.

Talk Track: Advanced Technical/Research

Talk Technical Level: 4/7

Talk Abstract:
TBA

What You’ll learn:
How to best practice data-centric AI in real-world ML projects. This covers automated methods to check the dataset for various issues common in ML data as well as how to efficiently address the issues to improve the dataset and subsequent ML model. I will cover novel algorithms invented by our research team and case studies which showcase the benefits of data-centric AI in real-world ML applications.

The intended audience is folks with experience in supervised learning who want to develop the most effective ML for messy, real-world applications. Some of the content will be technical, but not require a deep understanding of how particular ML algorithms/model work (having completed one previous ML course/project should suffice). The topics should be of interest to anybody working in: computer vision, natural language processing, audio/speech or tabular data, and other standard supervised learning applications, as well as DataOps folks.

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

Presenter:
William Falcon, Founder & CEO, Lightning AI

About the Speaker:
William Falcon is the creator of PyTorch Lightning, a deep learning framework. He is also the founder and CEO of Lightning AI, and was previously a co-founder and CTO of NextGenVest. He began working on these projects while completing a Ph.D. at NYU, which was funded by Google DeepMind and the NSF. Additionally, he worked as a researcher at Facebook AI and at Goldman Sachs.

Talk Track: Advanced Technical/Research

Talk Technical Level: 5/7

Talk Abstract:
As artificial intelligence (AI) continues to reshape industries and drive transformative advancements, the need for accelerated AI innovation has become paramount. Organizations across the globe are seeking novel ways to leverage AI technologies to gain a competitive edge, optimize processes, and deliver enhanced customer experiences. However, as AI applications grow in complexity and scale, so do the challenges related to scalability and security.

This talk aims to address the critical aspects of accelerating Enterprise AI innovation and will delve into the following key topics:

– Harnessing the Power of PyTorch and PyTorch Lightning: We will explore the benefits of modern AI frameworks, such as PyTorch, PyTorch Lightning, and Fabric, and the techniques that facilitate rapid development and deployment of AI solutions.
– Scalability: The exponential growth of data and increasing demands for AI applications necessitate scalable architectures. We will discuss strategies for designing AI systems that can effortlessly handle massive datasets and adapt to the evolving requirements of AI-driven applications.
– Security Considerations in AI: The integration of AI technologies introduces new security risks. This talk will highlight best practices for safeguarding AI systems against potential threats, ensuring data privacy, and maintaining compliance.
– Collaborative Ecosystems for Innovation: Accelerating AI innovation requires collaboration between diverse stakeholders, including researchers, developers, policymakers, and businesses. We will explore successful collaborative models that foster innovation.

What You’ll Learn:
TBA

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

Presenter:
Norm Zhou, Engineering Manager, Meta

About the Speaker:
Innovative, adaptable, and highly technical leader. Always looking holistically to make the highest impact starting from first principle. My career has lead me over many interesting challenges and environments. From hardware Chip architecture in ASIC and FPGA startups, to Ads then AI Platforms in large internet companies. Currently I am leading multiple teams working on AutoML to Democratize AI at Meta.

I am interested in maximizing my impact on this world by working both on cutting edge research and translational work that improves people’s lives in the real world.

Talk Track: Business Strategy

Talk Technical Level: 4/7

Talk Abstract:
ML platforms help enable intelligent data-driven applications and maintain them with limited engineering effort. However the current approaches to ML system building is limited by the “Kaggle Paradigm” which focuses on the data to model transformation and the operationalizing the deployment of models into applications. This model centric view limits further increase engineering productivity for future ML Systems. We propose an alternative policy centric view as an alternative to model centric view. This policy centric view involves two major additions to model centric view. First is a fully managed unified data collection system extending upstream to establish a “full chain of data custody”. Second we propose downstream extension to A/B testing systems which will bridge online offline mismatch typically experienced in many ML practitioners. Together these approaches enable a fully end-to-end automation allowing for a future ML platform to directly improve business metrics and more fluently address changing business needs.

What You’ll Learn:
How to best practice data-centric AI in real-world ML Connecting ML to Business Impact. Short comings of a model first approach and an proposed alternative.

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

Presenter:
Ryan McClelland, Research Engineer, NASA Goddard Space Flight Center

About the Speaker:
From a young age, Ryan McClelland has been captivated by futurism and technology, aspiring to contribute to a brighter future. As a Research Engineer in NASA GSFC’s Instrument Systems and Technology Division, he pursues the development and implementation of digital engineering technologies for space-flight mission. Ryan is particularly excited about the potential of Artificial Intelligence, Virtual Reality, Generative Design, and Digital Manufacturing to accelerate space systems development.

With a diverse background in technology development, Ryan’s previous research encompasses lightweight X-ray optics, aluminum foam core optical systems, and the investigation of non-linear effects in kinematic mechanisms. In addition to his research, Ryan has played a significant role in various flight missions, including designs currently on orbit aboard the Hubble Space Telescope and International Space Station. Recently, he served as the Roman Space Telescope Instrument Carrier Manager. Ryan holds a B.S. in Mechanical Engineering, summa cum laude, from the University of Maryland.

Talk Track: Case Study

Talk Technical Level: 4/7

Talk Abstract:
Come get a first hand account of how NASA is leveraging Generative Design to reduce the cost and increase the performance of spaceflight missions. Also, how the concept of AI Prompt Engineering can be practically applied to diverse fields, such as structures development.

What You’ll learn:
TBA

Talk: Supercharging Search with LLMs: The Instacart Journey

Presenter:
Prakash Putt, Staff Software Engineer, Instacart

About the Speaker:
TBA

Talk Track: Case Study

Talk Technical Level: 4/7

Talk Abstract:
Discover how Instacart’s search journey has been revolutionized through the implementation of Language Models (LLMs). By leveraging the power of LLMs, we have achieved significant enhancements, transforming the search experience for Instacart users. Join us to discover real-world use cases, gain insights into our seamless integration strategies, and witness how LLMs have empowered us to overcome challenges, deliver personalized recommendations, and elevate the overall search experience at Instacart.
An example

What You’ll Learn:
The audience will gain valuable insights into the practical implementation of LLMs in real production use cases. While LLMs have garnered significant attention, their effective integration into live environments remains a challenge. By attending this talk, participants will learn firsthand about the successful utilization of LLMs to enhance Instacart’s search journey. Through real-world examples, they will discover how LLMs can be harnessed to supercharge search capabilities and derive actionable knowledge for their own production scenarios.

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