ODSC AI West 2026 Conference | Open Data Science Conference
Reserve your place alongside like-minded individuals, practitioners, business leaders, and others reskilling in AI.
Master the Future of Applied AI
Stop following the hype and start building the future. ODSC AI West 2026 (San Francisco, CA, happening on Oct 27-29) is the leading technical conference for data scientists, engineers, and strategic leaders who need to implement cutting-edge AI today. We bypass high-level talk to provide three days of deep-dive, hands-on technical training from the world’s leading AI pioneers.
Lineup
Dr. Jon Krohn, Host | Co-Founder at SuperDataScience Podcast | Y Carrot
Michelle Yi, Co-Founder at Generationship
Cal Al-Dhubaib, Principal Technologist at Rubrik
Parth Sareen, Software Engineer at Ollama
Steven Pousty, PhD Principal Community Architect at Red Hat
Chip Huyen, Author | AI Engineering
Good to know
Highlights
- 2 days 8 hours
- In person
Refund Policy
Location
Hyatt Regency San Francisco Airport
1333 Old Bayshore Highway
Burlingame, CA 94010
How do you want to get there?
Agenda
OpenAI Agentic Vision
Enterprises are rapidly moving from generative AI experimentation to a new phase of transformation: agentic AI systems that can reason, take action, and complete multi-step workflows across business tools, data, and teams. Yet many organizations still struggle to convert AI pilots into measurable business value because of fragmented data, siloed systems, limited production know-how, and insufficient governance. This session will help attendees understand how enterprises can bridge the gap between frontier model capabilities and scalable deployment. We will examine the evolution from conversational AI to reasoning systems and agentic workflows, then explore practical patterns for building agents that operate across enterprise surfaces such as productivity tools, developer environments, browsers, desktops, APIs, and customer-facing products.
Context Engineering: How machines remember and forget?
Context Engineering is the art of shaping what an AI model knows at any moment by managing how information enters, persists, or fades from its working memory. In this session, we explore how machine learning systems “remember” through state objects, notes, and retrieval—and how they “forget” using compression, selection, and context limits. We’ll walk through real-world agent patterns that balance personalization with privacy, performance, and relevance. Participants will learn practical techniques to design memory that feels intentional, evolving, and human-aware.
Reasoning Models for Physical AI
Reasoning models for Physical AI aim to enable embodied agents—such as robots and autonomous vehicles—to perceive, understand, and act in the real world through contextual and causal reasoning. In this talk, I’ll provide an overview of foundational concepts in reasoning-centric AI for physical systems, highlighting the emergence of chain-of-thought reasoning in autonomous driving models such as NVIDIA’s Alpamayo family, which bridges human-like reasoning with trajectory planning to improve handling of complex scenarios. I will discuss model design principles, training strategies that promote causal reasoning, tools and datasets for development and evaluation, and open challenges at the intersection of physical reasoning, safety, and real-world deployment.