SHIPPING AI IS OPS, NOT NOTEBOOKS: A PRACTICAL MLOPS BLUEPRINT
A hands-on blueprint shows how to run AI systems reliably using containers, a registry, and multi-service orchestration.
A hands-on blueprint shows how to run AI systems reliably using containers, a registry, and multi-service orchestration.
Most failures come from environment drift, weak observability, and ad‑hoc deploys, not from model accuracy.
Clear container boundaries and a registry cut rollback risk and make scaling predictable.
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terminal
Split training vs inference into separate Docker images, then load test inference while upgrading training deps to verify zero cross-impact.
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terminal
Stand up docker-compose with FastAPI, Redis, PostgreSQL, MLflow, and Prometheus; validate versioned rollbacks using the model registry.
Legacy codebase integration strategies...
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Wrap existing notebook workflows in Docker and promote images via CI; add MLflow tracking/registry before touching serving paths.
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Introduce compose/k8s gradually: start with inference + registry + metrics, then migrate feature and monitoring services.
Fresh architecture paradigms...
- 01.
Start with a minimal compose stack (API, cache, DB, MLflow, metrics) and enforce image immutability from day one.
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Design for separation of concerns: distinct containers for training, inference, and monitoring with explicit contracts and SLIs.