ODSC West is less than a week away and we have a packed lineup of training sessions, workshops, and talks on topics ranging from machine learning to data visualization. You can check out the full list here, but for now, we’ve provided a sneak peek of 10 of the sessions you won’t want to miss.

How Enterprises Succeed with MLOps at Scale: David Alsabery | Western Regional Director | Iguazio

To remain competitive in the current environment it’s essential that businesses have a strong foundation in MLOps. To encourage continuous innovation, the next step will be to make the MLOps process repeatable, scalable and reproducible.

On Learning-Aware Mechanism Design: Michael I. Jordan, PhD | Distinguished Professor | University of California, Berkeley | ACM/AAAI Allen Newell Award Laureate

Join this session to explore the interface between machine learning and microeconomics, including leader/follower dynamics in strategic classification, a Lyapunov theory for matching markets with transfers, and the use of contract theory as a way to design mechanisms that perform statistical inference.

Responsible AI a Global Imperative for Governments and Business – Now and the Future: Kay Firth-Butterfield | Head of AI & Machine Learning, Member, Executive Committee | World Economic Forum

Although the access to or use of AI, and the governance of such,  is not uniform across countries, it’s important to understand these intricacies so that investment in technology is not ultimately wasted. In this session, we’ll look at the current environment and explore how might this thinking mature into the future.

Making ML Scaling Easy: Ion Stoica, PhD | Professor, Director | University of California, Berkeley, RISELab

To address the issues associated with developing distributed workloads (to meet the demands of modern machine learning workloads, researchers at UC Berkeley have been working on several projects. In this talk, you’ll learn about two of those projects, Ray and Alpa, that dramatically simplify scaling ML workloads

Is My NLP Model Working? The Answer is Harder Than You Think: Graham Neubig, PhD | Associate Professor, Faculty | Carnegie Mellon University, NeuLab

Ensuring that NLP is functioning well so as to not cause major PR disasters, is essential as NLP applications become more prevalent in our daily life. This talk will address two possible issues: automatic evaluation of generated text, and automatic fine-grained analysis of NLP system results, which are some first steps towards a science of NLP model evaluation.

From AutoML to AutoMLOps: Yaron Haviv | Co-Founder & CTO | Iguazio

This session will explore the challenges in operationalizing machine learning and deep learning. You’ll cover the production-first approach to MLOps pipelines as well as various real-world implementations and examples, and discuss the different stages, including automating feature creation using a feature store, building CI/CD automation for models and apps, deploying real-time application pipelines, observing the model and application results, creating a feedback loop and re-training with fresh data.

Feathr: Scalable Feature Store that opens the Window to Infinite Possibilities: Mario Inchiosa, PhD | Principal Data Scientist Manager | Microsoft

This session will take you on a deep dive into the scalable open-source feature store, Feathr. You’ll explore rich UDF support, dynamic type casting, point-in-time joins, time-aware sliding window aggregation, support for derived features, support for advanced ML scenarios and much more!

Taming Large Language Models into Trustworthy Conversational Virtual Assistants: Monica S. Lam, PhD | Professor, Faculty Director | Stanford University, Open Virtual Assistant Lab (OVAL)

Join this talk to explore how we can use large language neural models, like GPT-3, to enable computers to more effectively converse with us. You’ll cover how we can tame these neural models into robust, trustworthy, and cost-effective conversational agents across all industries and languages.

Foundations of Deep Reinforcement Learning: Pieter Abbeel, PhD | Director, Co-Director | Berkeley Robot Learning Lab, Berkeley Artificial Intelligence (BAIR) Lab

Join this session to learn about the foundations of Deep Reinforcement Learning, including MDPs, DQN, Policy Gradients, TRPO, PPO, DDPG, SAC, TD3, model-based RL, as well as current research frontiers, from one of the leading experts in the field. 

Data Analytics at Scale: A Four-legged Stool: Michael Stonebraker, PhD | Adjunct Professor | MIT

This talk will address four tactics that enable successful enterprise analytics efforts: data integration, using analytics suites, an information discovery tool and data lakes and lake houses. 

Register for ODSC West soon

To check out all of the incredible sessions that will be at ODSC West this year be sure to grab your pass soon. There are only a few days left and our in-person passes are selling out!