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AI By the Bay is an independent, engineering-first conference for builders of full‑stack AI — applications, data, infrastructure, and developer tooling. Expect deeply practical talks, hands-on lessons, and real‑world case studies from engineers and open‑source leaders.
By the Bay (Framework Foundation) with the Konfy production team, in collaboration with the Bay Area developer communities that have sustained our events for 11+ years.
Software Engineers, AI/ML Engineers, Data Scientists, Data Engineers, DevOps/MLOps, Knowledge Engineers, Engineering Leaders, Researchers, Product Managers, and startup builders working on AI‑native systems.
Learn how to adapt your workflow to AI‑native development — coding, debugging, testing, and deployment with AI tools; optimize applications for performance (GPU, distributed systems); and build reliable, observable AI features. You’ll learn how to build and operate Reliable Enterprise AI with agentic AI patterns, agents memory, and MCP-powered integrations, following best practices in reliability, security, and governance.
• Peter Norvig (https://ai.bythebay.io/post/peter-norvig-the-present-and-future-of-programming-with-ai)- The present and future of programming with AI.
• Julien Le Dem (https://ai.bythebay.io/post/julien-le-dem-the-advent-of-the-open-data-lake)- The advent of the open data lake.
• Mary Grygleski (https://ai.bythebay.io/post/mary-grygleski-harnessing-event-driven-and-multi-agent-architectures-for-complex-workflows-in-gene)- Harnessing Event-Driven and Multi-Agent Architectures for Complex Workflows in Generative AI System.
Deep dives into data pipelines for AI: vector databases, streaming and real‑time ETL, feature stores, governance, and cost/performance optimization across cloud and on‑prem environments. You’ll learn how to build and operate Reliable Enterprise AI with agentic AI patterns, agents memory, and MCP-powered integrations, following best practices in reliability, security, and governance.
• Vinay Rao (https://ai.bythebay.io/post/vinay-rao-safety-in-foundational-models)- Safety in Foundational Models: Understanding AI Deployment Challenges
• Alex Merced (https://ai.bythebay.io/post/alex-merced-open-standards-and-lakehouse-ai-development-arrow-flight-apache-iceberg-and-mcp)- Open Standards and Lakehouse AI Development - Arrow Flight, Apache Iceberg and MCP.
• Danica Fine and Josh Reini (https://ai.bythebay.io/post/danica-fine-donut-kill-my-vibe-enriching-rag-with-apache-iceberg-for-sweet-structured-retrieval)- Donut Kill My Vibe: Enriching RAG with Apache Iceberg™ for Sweet, Structured Retrieval.
• Viktor Gamov (https://ai.bythebay.io/post/viktor-gamov-the-missing-protocol-how-mcp-bridges-llms-and-data-streams)- The Missing Protocol: How MCP Bridges LLMs and Data Streams.
Best practices for RAG, multimodal data processing, evaluation, prompt & model lifecycle, knowledge graphs, and domain‑specific AI knowledge representations. You’ll learn how to build and operate Reliable Enterprise AI with agentic AI patterns, agents memory, and MCP-powered integrations, following best practices in reliability, security, and governance.
• Matt White (https://ai.bythebay.io/post/matt-white-beyond-open-weights-how-open-source-and-open-standards-are-shaping-the-future-of-ai)- Beyond Open Weights: How Open Source and Open Standards Are Shaping the Future of AI.
• Donny Greenberg (https://ai.bythebay.io/post/donny-greenberg-beat-gpt-4-and-build-the-best-ai-for-you-mobilizing-first-party-data-with-fine-tu)- Beat GPT 4 and Build the Best AI For You: Mobilizing First-Party Data with Fine-Tuning.
Blueprints for CI/CD of models and agents, scalable model serving, orchestration, observability, and production SLOs for AI workloads. You’ll learn how to build and operate Reliable Enterprise AI with agentic AI patterns, agents memory, and MCP-powered integrations, following best practices in reliability, security, and governance.
• Mihai Maruseac (https://ai.bythebay.io/post/mihai-maruseac-taming-the-wild-west-of-ml-practical-model-signing-with-sigstore-on-kaggle)- Taming the Wild West of ML: Practical Model Signing with Sigstore on Kaggle.
• Steve Anderson (https://ai.bythebay.io/post/steve-anderson-trusted-agentic-ai-architecting-intelligent-systems-with-reliable-data-at-scale)- Trusted Agentic AI: Architecting Intelligent Systems with Reliable Data at Scale.
• Michael Maximilien (https://ai.bythebay.io/post/michael-maximilien-build-ai-agent-workflows-fast-for-any-framework-and-llms)- Build AI Agent workflows fast, for any framework, and LLMs.
• Robert Nishihara (https://ai.bythebay.io/post/robert-nishihara-scaling-multimodal-data-and-reinforcement-learning-with-ray)- Scaling Multimodal Data and Reinforcement Learning with Ray.
Three days total: a hands‑on workshop day, then two days of multi‑track talks and keynotes across themes like AI‑Native Coding, Knowledge/Data/Models, AI Data Infrastructure, and Open‑Source AI.
The AI By the Bay conference will be held from November 17-19, 2025. The first day is reserved for bespoke workshops to learn Reliable Enterprise AI. Tickets for workshops are sold separately. Main days will feature keynote sessions, breakout discussions, and networking opportunities. A detailed schedule will be available on our website closer to the event date.
We publish the full agenda—session titles, speakers, times, and rooms—on the website a few weeks before the conference and email all registered attendees as soon as it’s live.
In the meantime, browse talk previews and speaker spotlights on our blog.(https://ai.bythebay.io/allposts)
Yes — hallway track, topic tables/BoFs, and an evening reception to connect with speakers, attendees, and partners.
Yes — all conference talks will be published free of charge on our YouTube (https://www.youtube.com/@FunctionalTV/featured)channel after the event (within several weeks). Speakers may optionally share slides, which we will link where possible.
The conference is primarily in‑person.
Published videos include platform captions; we encourage accessible slide design and share materials when speakers consent.
Attendees include Software Engineers, AI/ML & Data Engineers, Data Scientists, MLOps/DevOps, architects, and leaders focused on building AI‑native products. They attend to learn best practices in coding with AI, scalable data/infra, model lifecycle, and real‑world deployment.
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