45% of enterprise AI projects fail because of data quality issues — not model accuracy. The root cause is deceptively simple: producers and consumers in streaming AI pipelines evolve independently, and without formal contracts, a silent schema change in one team's Kafka producer becomes a 3 AM page for another team's inference service.

This talk shows how to apply data contracts to event-driven AI architectures — the same discipline that API contracts brought to microservices, now enforced at the streaming layer. In a live demo with Debezium CDC, Kafka, and a downstream ML inference service, I'll walk through how schema compatibility rules catch breaking changes at produce time, not at prediction time. You'll see a schema evolution that would silently corrupt model inputs get rejected before it ever reaches the broker.

Attendees will leave with:
- A concrete pattern for implementing data contracts in streaming AI architectures
- A comparison of schema evolution strategies (Avro, Protobuf, JSON Schema) and when each fits ML workloads
- A reproducible setup they can deploy to enforce schema governance across their own pipelines