MLOps and Model Deployment Roadmap

Take a trained model to production: packaging, serving, pipelines, monitoring and retraining.

10 milestones · ~118 hours total

1. 1

   Python packaging and project structure

   ~10h

   Modules, virtual environments, pyproject.toml, linting and unit tests with pytest for ML code.

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2. 2

   ML basics you must know to deploy

   ~15h

   Train/validation/test splits, scikit-learn pipelines, serialization with joblib and avoiding data leakage.

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3. 3

   Git, DVC and reproducibility

   ~8h

   Version code and data, pin dependencies, seed randomness and make a training run repeatable.

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4. 4

   Experiment tracking with MLflow

   ~10h

   Log parameters, metrics and artifacts, compare runs and register a model version.

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5. 5

   Serving models with FastAPI

   ~12h

   Wrap a model in a REST endpoint, validate inputs with Pydantic and test latency locally.

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6. 6

   Docker for ML services

   ~10h

   Write a small image, manage dependencies, use multi-stage builds and run the container locally.

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7. 7

   CI/CD for ML

   ~12h

   GitHub Actions to run tests, build images and deploy; separating code changes from model changes.

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8. 8

   Pipelines and orchestration

   ~15h

   Build a training pipeline with Airflow or Prefect: scheduled runs, retries and dependencies.

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9. 9

   Cloud deployment basics

   ~12h

   Deploy the container on a managed service such as AWS ECS or Cloud Run, with secrets and environment config.

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10. 10

    Monitoring, drift and retraining

    ~14h

    Track latency and errors, detect data drift, define retraining triggers and plan safe rollbacks.

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