Curriculum

The MLOps Zoomcamp covers six main modules plus a final project. Each module has video lectures, hands-on material, and a homework assignment.

For the canonical curriculum (videos, code, exact homework questions), see the GitHub repository.

Modules

Module 1: Introduction

  • What is MLOps and why it matters.
  • MLOps maturity model.
  • The NY Taxi dataset used as the running example.
  • Course structure and environment setup.

Module 2: Experiment Tracking & Model Management

  • Experiment tracking with MLflow.
  • Saving and loading models.
  • The model registry.

Module 3: Orchestration & ML Pipelines

  • Turning notebooks into orchestrated ML pipelines.
  • Workflow orchestration.

Module 4: Model Deployment

  • Online vs. offline deployment.
  • Web service deployment with Flask.
  • Streaming deployment with AWS Kinesis and Lambda.
  • Batch scoring for offline processing.

Module 5: Model Monitoring

  • Monitoring ML services.
  • Web service monitoring with Prometheus, Evidently, and Grafana.
  • Batch job monitoring with Prefect, MongoDB, and Evidently.

Module 6: Best Practices

  • Unit and integration testing.
  • Linting, formatting, and pre-commit hooks.
  • CI/CD with GitHub Actions.
  • Infrastructure as Code with Terraform.

Final Project

  • An end-to-end project that integrates experiment tracking, orchestration, deployment, and monitoring.

Homework and project

Each module has a homework assignment. To earn the certificate, you also complete the final project during a live cohort. See Project for details.