Summary

This repository provides a comprehensive MLOps coding course designed to teach practical, hands-on Python development for AI and ML projects. It targets both beginners and experienced professionals, offering insights into building, developing, and maintaining state-of-the-art MLOps codebases, thereby enhancing practical skills in the field.

How It Works

The course adopts a project-driven learning methodology, emphasizing hands-on Python coding directly applicable to real-world AI scenarios. It covers core MLOps techniques, including CI/CD, software containers, model registries, and observability, utilizing a suite of industry-standard open-source tools. This approach aims to streamline the development and deployment lifecycle of ML models.

Quick Start & Requirements

To begin, clone the repository and install dependencies using uv run just install. The course material can be served locally via uv run just serve, accessible at http://localhost:8000/. Key prerequisites include Python and the uv package manager. Additional resources like example Python packages and templates are linked within the README.

Highlighted Details

• Focuses on hands-on Python coding for AI projects.
• Employs project-driven learning with practical instructions.
• Covers advanced MLOps techniques: CI/CD, containers, model registries, observability.
• Integrates industry-standard tools: uv, pytest, MLflow, bandit, pre-commit, GitHub, VS Code.
• Offers optional mentoring sessions and a premium MLOps Coding Assistant.

Maintenance & Community

The course is open-source and welcomes community contributions to documentation, examples, and bug fixes. Support for creators is available through donations via Stripe.

Licensing & Compatibility

Licensed under CC-BY 4.0, this course permits commercial use and adaptation, provided proper attribution is given.

Limitations & Caveats

The MLOps Coding Assistant is a premium, subscription-based tool. Group and organizational training require custom quotes, indicating that some advanced support or features are not universally self-serve.