San Francisco AI Engineering & Infrastructure Summit 2026

Beat the rush and join us early for complimentary barista-made coffee and breakfast.

Insights into incorporating ethical considerations into AI engineering practices, including bias mitigation, transparency, and accountability.

A case study on designing efficient data pipelines to handle large-scale data ingestion, processing, and storage for AI applications.

Discussing the latest advancements in hardware and cloud infrastructure that enable high-performance AI computations.

A case study on designing efficient data pipelines to handle large-scale data ingestion, processing, and storage for AI applications.

In this innovative session, attendees will be faced with a series of scenarios that they may face in their roles. Attendees will discuss the possible courses of action with their peers to consider the ramifications of each option before logging their own course of action.

Results will be tallied and analysed by our session facilitator and results will impact the way the group moves through the activity.

Will we collectively choose the right course of action?

As we deployed AI agents to automate accounting workflows in a highly regulated environment, we quickly learned that model-level safeguards weren’t enough. This session explores how we built a layered safety chain, spanning the user’s role, the agent, and the underlying services and data, to manage blast radius, permissions and autonomy in production. We’ll share how targeted beta rollouts and deliberate UI design helped define clear boundaries between essential human oversight and safe, scalable automation.

AI that looks brilliant in a demo often struggles in the real world. In this keynote, I’ll share what a research engineering team learned translating promising AI prototypes into practical organisational capability, and why success depends less on model choice than on delivery discipline, platform thinking, governance, and trust.

• Why data, workflows, governance, and operating model matter more than most teams realise
• How to run fast, safe experimentation without undermining reliability or security
• What organisations need to get right before AI can deliver lasting operational value

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Exploring the opportunities and challenges of deploying AI models on edge devices for low-latency and offline capabilities.

Select a topic of discussion and engage in an interactive roundtable discussion with a group of your like-minded peers.

Put your knowledge to the test in this fast-paced quiz covering real-world trivia, key concepts, and emerging trends. Compete for bragging rights - and a travel voucher - as the top scorer takes the crown.

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An exploration of the complexities involved in engineering scalable AI systems, including data pipelines, model deployment, and real-time processing.

A lively session on whether cloud-based or on-premises infrastructure offers the optimal environment for AI development and deployment.

This interactive session puts these questions directly to the audience. Participants vote live on a series of real-world scenarios, explore the results together, and vote again as perspectives shift through the discussion.