Trustworthy AI Systems: Proven Production Strategies

Most AI demos look great. Few survive contact with real data, real users, and real failure modes.

On August 13 in Redmond, WA, join Muazma Zahid (Group Product Manager, Google BigQuery, formerly Microsoft Azure) for an in-person session on what actually separates a trustworthy AI system from a fragile one.

You'll walk away with:

Where trust breaks down beneath the model layer — data, grounding, evaluation, orchestration

Grounding techniques that keep outputs accurate and verifiable

Evaluation frameworks for measuring performance before and after deployment

Observability practices to catch drift and failures early

Design patterns for reliable multi-agent orchestration

A framework to audit your own AI stack for reliability risks

Best for data scientists, ML engineers, AI product managers, and engineering leads moving systems past the prototype stage.