What is the MLOps Guide Website?

MLOps Guide (mlops-guide.github.io) is a free, English-language educational site that explains MLOps concepts and walks through a complete end-to-end implementation. It is aimed at projects and companies that want to build a more reliable MLOps environment, and it pairs the theory behind MLOps with a tutorial you can follow. The guide was made by Arthur Olga, Gabriel Monteiro, Guilherme Leite, and Vinicius Lima.

What the guide covers

The site is organized around two halves: principles and implementation.

Principles of MLOps introduces the core ideas:

• Versioning
• Automation
• Feature storing
• CI/CD for machine learning
• Continuous monitoring

Implementation Guide turns those ideas into a working setup:

• Introduction and project structure
• Starting a new project with Cookiecutter
• Environment setup, including IAM access and provisioning the IBM environment with Terraform
• Managing the deployment space
• Data versioning and pipelines with DVC
• Deployment with Watson Machine Learning
• CI/CD: implementing tests, continuous integration with CML and GitHub Actions, and continuous delivery with CML, GitHub Actions, and Watson ML
• Monitoring with IBM OpenScale

Tools discussed

The guide is built around a specific toolchain, summarized in its own tools table:

How modular is it?

The guide describes itself as modular: a team can choose to swap tools at any point based on project preferences and use cases. The architecture diagram shows the complete MLOps flow used in the tutorial, and the site notes that the exact end-to-end project is available as a companion example. That means you can follow the full walkthrough as written, or use it as a reference architecture and substitute tools that fit your stack.

Who it is for

This is a good starting point if you are:

• New to MLOps and want a structured explanation of the concepts before touching tooling
• Setting up a first MLOps environment and looking for a concrete, ordered sequence of steps
• Evaluating how versioning, CI/CD, deployment, and monitoring fit together in one pipeline

It is less suited to readers looking for a tool-agnostic deep dive or for production hardening guidance beyond the tutorial's scope. The guide is explicit that it presents one implementation intended to fit most use cases, not an exhaustive survey of every option.