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MLOps: Deploying Machine Learning & Deep Learning Models

ML Pipelines and CI/CD

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

Level
Intermediate
Lessons
3
Total time
47 min
Simulations
2
Included in MLOps: Deploying Machine Learning & Deep Learning Models · $9
Tools you'll use
  • Python
  • Docker
  • FastAPI
  • Kubernetes
  • GitHub
Skills
  • MLflow
  • Model registry
  • CI/CD

What you will be able to do

  • Track every experiment and released model
  • Automate model training and validation

Before you start

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

  • packaging-and-serving-models
Modules that help first

Syllabus

3 lessons · 47 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. Experiment Tracking and the Model Registry

    16 min

    Logging every run's parameters, metrics and artefacts automatically, then promoting a model to production through a registry — not by overwriting a file.

    Included with the track Simulation

  2. Training Pipelines

    16 min

    Six repeatable steps from raw data to a validated model — and why data validation and preprocessing inside the model are the parts that save you.

    Included with the track Simulation

  3. CI/CD for Machine Learning

    15 min

    Testing code, data and model behaviour before every release — and a gate that compares the challenger with the champion on a frozen test set.

    Included with the track
Lifetime access
$9
Buy the track
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ML Systems in Production

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

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Packaging and Serving Models

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

  • 3 lessons
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Included in MLOps: Deploying Machine Learning & Deep Learning Models· $9
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Serving Deep Learning Models and LLMs

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

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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