Top Speakers at ODSC West: Innovators Leading the Future of AI and Data Science

ODSC West is right around the corner, promising an impressive lineup of industry leaders who will cover cutting-edge developments in AI, machine learning, and data science. Many of these speakers are familiar faces at past ODSC events or are regular contributors to major AI and tech conferences.

So the team has decided to highlight some of the key speakers and their sessions that will interest those who want to see how AI will shape the future and data science.

Sergey Levine PhD’s Keynote: Reinforcement Learning with Large Datasets — A Path to Resourceful Autonomous Agents

This keynote will be delivered by a top AI expert, delving into how reinforcement learning (RL) with large datasets is paving the way for more autonomous and resourceful agents. The session will focus on real-world applications, addressing how RL can be leveraged in complex environments where traditional rule-based systems fall short. Attendees can expect insights on the future of AI agents and how RL can unlock their full potential.

Matt Harrison’s Talk: Machine Learning with XGBoost

XGBoost is one of the most widely used machine learning algorithms today, known for its speed and accuracy in decision tree-based models. The speaker for this session will walk through advanced XGBoost techniques, sharing strategies for optimizing model performance. Whether you are scaling your models or looking for ways to enhance predictive accuracy, this session will offer valuable guidance for data science professionals.

Sinan Ozdemir’s Talk: LLMs: From Prototype to Production — LLMOps, Prompt Engineering, and Moving LLMs to the Cloud

With large language models (LLMs) becoming a central part of modern AI applications, this speaker will provide a roadmap from prototyping to deploying LLMs in production environments. The session will cover key topics like LLMOps, prompt engineering, and transitioning LLMs to the cloud, offering practical takeaways for data scientists aiming to implement LLMs at scale.

Michelle Yi and Amy Hodler’s Talk: Causal Graphs: Applying PyWhy to Go Beyond Explainability

This session features two expert speakers who will explore how causal graphs, specifically using the PyWhy library, can push the boundaries of AI explainability. By integrating causal inference techniques, attendees will learn how to move beyond traditional model explanations, allowing for more accurate insights into model behavior. This session will provide a deep dive into both the theory and practice of causal graphs in real-world data science scenarios.

Chandra Khatri’s Talk: State of the Art in Generative AI: From LLMs to SLMs to Large Multimodal Models to Autopilot to AI Agents

Generative AI is evolving rapidly, and this session will present the latest advances in LLMs, small language models, and multimodal models. The speaker will also cover autopilot features and AI agents, showing how these technologies are converging to transform industries. Attendees will gain a comprehensive understanding of the current state of generative AI and where it’s heading next.

Laurie Voss’s Talk: RAG in 2024: Advancing to Agents

Retrieval-Augmented Generation (RAG) is an important component of modern AI, especially as models evolve toward autonomous agents. This session will focus on the 2024 advancements in RAG, providing insights into how this technology is enabling AI systems to perform complex tasks that require retrieving external data sources. If you’re interested in building AI systems that can interact with vast datasets dynamically, this session is not to be missed.

Stefanie Molin’s Talk: Data Morph: A Cautionary Tale of Summary Statistics

Data science often relies on summary statistics, but this session will caution against over-reliance on them. The speaker will discuss the limitations of summary statistics and how they can lead to incorrect conclusions if not carefully analyzed. By using real-world examples, this session will provide data scientists with the tools to avoid common pitfalls in data analysis.

Julien Simon’s Talk: Building High-Quality Domain-Specific Models with MergeKit: A Cost-Effective Approach Using Small Language Models

This session introduces MergeKit, a tool designed to build high-quality, domain-specific models using small language models. The speaker will discuss how this tool offers a cost-effective alternative to large models without compromising quality. If you’re working in a specialized field and looking for efficient ways to develop models, this talk will offer actionable strategies to implement in your projects.

Paige Bailey’s Talk: Data Science in the Age of Generative AI

Generative AI is not just a buzzword; it’s reshaping the way data scientists work. In this session, the speaker will discuss the implications of generative AI in the field of data science, from how data is generated to how it’s analyzed. You’ll learn about the tools and techniques that are at the forefront of this transformation and how you can stay ahead in this rapidly changing landscape.

Lin Qiao’s Talk: Compound AI Systems and the Future of AI Integration

As AI systems become more complex, the need for integration between different AI components becomes critical. This session will focus on how compound AI systems, which combine multiple models and algorithms, are the future of AI. The speaker will provide insights into how these systems can be integrated efficiently and what challenges data scientists should expect when working with compound AI.

Last Chance to Attend ODSC West And Be At the Forefront of AI

ODSC West is your opportunity to hear from these thought leaders and gain insights into the latest trends and tools in AI and data science. With sessions covering a wide range of topics, from reinforcement learning to causal graphs and generative AI, this is an event no data professional should miss.

Spots are filling fast, so secure your pass today to stay ahead in the world of AI and data science!