Trustworthy AI Systems: Proven Production Strategies

Every AI system looks good in a demo. Few survive contact with real data, real users, and real failure modes. Join us in Redmond, WA on August 13 for a session with Muazma Zahid, Group Product Manager at Google, on what it actually takes to build AI systems that hold up in production.

This is part of Data Science Dojo's series on applied AI engineering. We're going past model capability into the layers that decide whether a system survives once it's live: data quality, grounding, evaluation, observability, and agent orchestration.

What You'll Learn

• Where trust actually breaks down in AI systems — not at the model layer, but underneath it
• How data quality issues quietly undermine even capable models in production
• Grounding techniques that keep model outputs accurate and tied to verifiable information
• Evaluation frameworks for measuring model and system performance before and after deployment
• Observability practices for catching drift and failures early
• Design patterns for reliable agent orchestration across models, tools, and agents
• A framework for spotting where reliability risks are hiding in your own AI stack

Why This Matters
The gap between "impressive" and "trustworthy" is one of the most consequential engineering challenges teams face right now. A model that performs well in testing can still fail once it's grounded in messy data or embedded in a multi-agent pipeline. Engineering discipline — systematic evaluation, production observability, thoughtful orchestration — is what separates teams shipping with confidence from teams constantly firefighting.

Who Should Come?
Data scientists, ML engineers, AI product managers, and engineering leads taking AI systems past the prototype stage. This is built for people already building or maintaining production AI or agentic systems who want practical patterns, not an intro-level overview.

Bring your questions — we'll leave time for live Q&A.