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MLOps: Deploying Machine Learning & Deep Learning Models

Packaging and Serving Models

From notebook to service: saving models, an API with FastAPI, and reproducible Docker containers.

Level
Intermediate
Lessons
3
Total time
48 min
Simulations
—
Included in MLOps: Deploying Machine Learning & Deep Learning Models · $9
Tools you'll use
  • Python
  • Docker
  • FastAPI
  • Kubernetes
  • GitHub
Skills
  • FastAPI
  • Docker
  • Model serialisation

What you will be able to do

  • Serve a model as a REST API
  • Build a reproducible Docker image

Before you start

A suggestion, never a lock — you can start here.

  • ml-systems-in-production
Modules that help first

Syllabus

3 lessons · 48 min

Every lesson's title and summary is public. The lessons themselves open once you own the track.

Every lesson in MLOps: Deploying Machine Learning & Deep Learning Models

  1. Saving Models Reproducibly

    14 min

    Artefacts, dependency versions, and why `model.pkl` alone is not enough to load the same model three months later.

    Included with the track

  2. A Model API with FastAPI

    18 min

    A prediction endpoint with input validation, a health check and the model version in every response — a service other teams can call without knowing what scikit-learn is.

    Included with the track

  3. Containerising with Docker

    16 min

    An image that is small, fast to rebuild, and runs exactly the same on a laptop, in CI and on a server — because the environment ships with the code.

    Included with the track
Lifetime access
$9
Buy the track
More in MLOps: Deploying Machine Learning & Deep Learning Models
Module · Intermediate

ML Systems in Production

Why models degrade after launch, how batch and online inference differ, and what to monitor.

  • 4 lessons
  • 1h 4m
  • 4 simulations
Included in MLOps: Deploying Machine Learning & Deep Learning Models· $9
Module · Intermediate

Serving Deep Learning Models and LLMs

GPUs, batching, quantisation and formats like ONNX — making large models fast enough and cheap enough.

  • 4 lessons
  • 1h 6m
Included in MLOps: Deploying Machine Learning & Deep Learning Models· $9
Module · Intermediate

ML Pipelines and CI/CD

Experiment tracking, model registries, and automated pipelines from raw data to a released model.

  • 3 lessons
  • 47 min
  • 2 simulations
Included in MLOps: Deploying Machine Learning & Deep Learning Models· $9
Module · Intermediate

Monitoring and Retraining

Drift detection, performance with delayed labels, retraining strategies, and safe rollouts.

  • 3 lessons
  • 48 min
  • 4 simulations
Included in MLOps: Deploying Machine Learning & Deep Learning Models· $9