Environment Setup

The MLOps Zoomcamp uses Python with common ML and MLOps tooling: MLflow, Prefect, Docker, Evidently, Grafana, Prometheus, Terraform, and AWS for cloud examples. This page covers the high-level setup decisions. For step-by-step setup, follow the videos in Module 1.

Where to run the course

You can work locally or on a cloud VM. Module 1 demonstrates both:

• A local machine with Python and Docker installed.
• GitHub Codespaces.
• A cloud VM (the course shows an AWS EC2 instance).

A cloud VM is convenient because the later modules use AWS services anyway, and it keeps your environment consistent.

Python and dependency management

The course uses Python with the scientific stack (pandas, scikit-learn) plus MLOps libraries. You can manage dependencies with uv, conda, or pip + venv. The choice does not affect the course content.

On Windows, Anaconda is often the easiest way to get Python plus scientific libraries running. Docker Desktop covers the container side.

Docker

Docker is used throughout the course for packaging models, running services, and reproducing environments. Install Docker and Docker Compose early. Module 1 includes the installation steps.

Cloud (AWS)

The course uses AWS for several modules:

• Module 4 deploys a streaming service with Kinesis and Lambda.
• Module 6 provisions infrastructure with Terraform.

New AWS accounts get free tier credits. You introduce each service as the modules reach it, so you do not need prior AWS experience.

Tools introduced per module

• Module 2: MLflow.
• Module 3: a workflow orchestrator.
• Module 4: Flask, AWS Kinesis, AWS Lambda, Docker.
• Module 5: Evidently, Grafana, Prometheus, Prefect, MongoDB.
• Module 6: pytest, pre-commit, GitHub Actions, Terraform.

Each module README lists exactly what to install for that module.