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Production deployment and delivery

Release an AI service with a rollback path

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What you will learn

Containers, CI/CD, Observability, Canaries, Incident response.

Engineering the capability

A reproducible release packages application code, dependencies, configuration schema, prompt version, and retrieval/index compatibility. Pin the environment so a new machine can run the same checks. Keep runtime secrets outside the image and fail startup clearly when required configuration is missing.

Run unit checks for deterministic logic, integration checks for adapters, and an evaluation gate for generated behavior. A deployment can pass compilation while degrading answer quality. Use a small rollout and compare meaningful metrics before expanding traffic.

Observe request errors, latency, workload, evaluation signals, and spending. Alerts need owners and actions. A dashboard without a response procedure is insufficient during an outage. Rollback may require reverting a prompt or index alias as well as application code; rehearse the combination in a safe environment.

Worked case

A new embedding version changes vector dimensions. Deploying its query encoder before rebuilding and switching the compatible index breaks retrieval. Build the new index separately, validate it, switch encoder and index together, and retain the previous pair for rollback. Record the pair in the release manifest.

Put it into practice

Continue with the next lab: ship and roll back a knowledge API. Build the artifact, record the failure cases, and explain the tradeoff before moving on.

YOUR LEARNING JOURNEY

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Release an AI service with a rollback path | Agentic AI | Android Engineers