Building Production-Grade AI Systems

Purpose

Enable engineering and platform teams to ship AI systems that survive production pressure. This session assumes systems will change — and designs for that reality.

Session focus

• Turning models into accountable services
• Designing for monitoring, rollback, and recovery
• Embedding governance without slowing delivery

Working blocks

• From model to system: Serving patterns (APIs, batch, async) and why deployment is the beginning.
• Operational accountability: Decision boundaries, monitoring what matters, and response playbooks.
• System memory and learning loops: What feedback is captured and what actually improves over time.
• Escalation and failure handling: When systems stop acting and how humans step in.

Teams leave with

• Clear production patterns they can apply immediately
• A shared view of what “done” actually means
• Fewer surprises after launch