ODSC West is less than a month away! Get ready for 300+ hours of hands-on training sessions, workshops, and talks on Generative AI, LLMs, MLOps, Machine Learning, Deep Learning, and more. We can’t possibly feature all of our incredible sessions here, but read on for a representative list of the unique options that ODSC West will feature.
MLOps: Monitoring and Managing Drift
Oliver Zeigermann | Machine Learning Architect | Freelancer
In this 2 part workshop, you’ll simulate production on a machine learning model, detect drift, and analyze and decide on the necessary steps to resolve it. By the end of this session, you will be equipped with the knowledge and practical tools you need to manage your ML models in the real world.
What is a Time-series Database and Why do I Need One?
Jeff Tao | Founder & CEO | TDengine
With the advent of IoT and the cloud, the volume of time-series data has begun growing exponentially in an unprecedented way, representing a major challenge for general database management systems like relational and NoSQL databases. Purpose-built time-series databases, on the other hand, are optimized to handle the special characteristics of time-series data, making them more efficient in terms of ingestion rate, query latency, and data compression.
AI and Video Games: The Evolution
Jack McCauley |Board Trustee at University of California, Berkeley | Former co-founder and Engineer, Oculus VR | Faculty Member Jacobs Institute | McCauley Chair in Drug Policy Innovation at RAND Corporation | MSRI Trustee | Black Lab LLC
Dive into the history of Neural Networks and explore the ways that video games laid the groundwork for the AI that we have today. You’ll discuss tensors, neural networks, GPUs and much more.
Data Morph: A Cautionary Tale of Summary Statistics
Stefanie Molin | Software Engineer, Data Scientist, Chief Information Security Office | Bloomberg | Author of Hands-On Data Analysis with Pandas
In this talk, you will explore “Data Morph,” an open-source package that builds on previous research from Autodesk using simulated annealing to perturb an arbitrary input dataset into a variety of shapes, while preserving the mean, standard deviation, and correlation to multiple decimal points. You will see how it works, discuss the challenges faced during development, and explore the limitations of this approach.
Understanding the Landscape of Large Models
Lukas Biewald | CEO and Co-founder | Weights & Biases
Join this session to explore the current landscape of large models from GPT-3 to Stable Diffusion. You’ll also discuss how the teams behind some of the open-source projects are using W&B to accelerate their work.
MLOps v LMOps – What’s Different?
Robert Crowe | Product Manager, MLOps and TF OSS | Google
The field of MLOps arose to address the need for machine learning technology equipped with a rigorous approach and production-ready systems. Now, the advent of large models has necessitated the emergence of LMOps. In this session, you’ll explore the use of ML pipeline architectures for implementing production ML applications, including large model architectures.
Troubleshooting and Measuring Embedding/Vector Drift for Production Deployments of Language Models
Amber Roberts | Data Scientist, Growth Lead | Arize AI
In this presentation, Amber Roberts, Machine Learning Engineer at Arize AI, will present findings from research on ways to measure vector/embedding drift for image and language models. With lessons learned from testing different approaches (including Euclidean and Cosine distance) across billions of streams and use cases, Roberts will dive into how to detect whether two unstructured language datasets are different — and, if so, how to understand that difference using techniques such as UMAP.
Peter Norvig | Engineering Director at Google | Education Fellow at Stanford Institute for Human-Centered Artificial Intelligence (HAI)
We have seen amazing technical progress in AI applications in recent years. This talk considers the human side rather than the technical side: how can we gain confidence that our applications will be fair, just, truthful, beneficial, and well-stirred for their users, the other stakeholders, and society at large.
Implementing Gen AI in Practice
Yaron Haviv | Co-Founder and CTO | Iguazio
In this session, you’ll explore 3 real-world examples of Gen AI applications and discuss best practices on how to create a reproducible process for rapid development and deployment without breaking the bank and take into consideration modularity and safety concerns.
Graphs: The Next Frontier of GenAI Explainability
Amy Hodler | Founder, Consultant | GraphGeeks.org
Michelle Yi | Board Member | Women In Data
This talk will examine the implications of incorporating graphs into the realm of generative AI. Learn about foundational concepts such as directed acrylic graphs (DAGs), Jedeau Pearl’s “do” operator, and keeping domain expertise in the loop. You’ll hear how the explainability landscape is evolving, comparisons of graph-based models to other methods, and how we can evaluate the different fairness models available.
Join us as we unravel the transformative potential of graphs and their impact on predictive modeling, explainability, and causality in the era of generative AI.
To attend these and all of our expert-led sessions, join us in the heart of Silicon Valley for ODSC West 2023 from October 30th to November 2nd. Register now–40% off ends soon.