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Stop 17 · MLOps & Model DeploymentTopics · 2/10

Docker for ML Models

Part of the AI Engineer Roadmap

In short

Packaging a model and its dependencies into a container so it runs identically across dev, staging and production — solves the classic 'works on my machine' problem for ML services.

How to learn this

  1. 1Containerize a model-serving API with a Dockerfile.
  2. 2Learn to keep image size manageable (multi-stage builds, minimal base images) for faster deploys.
  3. 3Run the container locally and confirm behavior matches your local dev environment.

More topics at this stop

Model Serving (FastAPI/Flask)Model VersioningExperiment Tracking (MLflow, Weights & Biases)CI/CD for ML PipelinesModel Monitoring & Drift DetectionA/B Testing ModelsBatch vs Real-time InferenceKubernetes for ML (Overview)Feature Stores (Overview)

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