What the MLOps Guide Covers and Where to Start

The MLOps Guide (mlops-guide.github.io) is a free, English-language resource that combines MLOps theory with a hands-on tutorial for building a working MLOps environment. It is aimed at projects and companies that want a more reliable MLOps setup, and it is written so that most use cases can adapt it. The guide is modular: you can follow it end to end or swap tools at any point to match your own project preferences.

What the Guide Is

According to the site, the guide has two purposes:

• Explain the theory behind MLOps.
• Provide an implementation that should fit most use cases.

It was made by Arthur Olga, Gabriel Monteiro, Guilherme Leite, and Vinicius Lima. The site describes MLOps as a methodology of operation that brings DevOps practices to the machine learning model lifecycle — making it easier to move an experimental model into production and maintain it efficiently.

Core MLOps Concepts Covered

The guide defines the main features of an MLOps project as:

• Data and model versioning
• Feature management and storing
• Automation of pipelines and processes
• CI/CD for machine learning
• Continuous monitoring of models

These map directly onto the guide's section structure, so the theory sections and the tutorial reinforce each other.

Tutorial Modules and Sections

The guide is organized into a principles part and an implementation part.

Principles of MLOps

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

Implementation Guide

• Introduction — tools and project structure
• Starting a new project with Cookiecutter
• Environment — IAM access, setting up the IBM environment with Terraform, managing the deployment space
• Versioning — what DVC is, data versioning, working with pipelines
• Deployment with Watson Machine Learning
• CI/CD for machine learning — implementing tests, continuous integration with CML and GitHub Actions, continuous delivery with CML, GitHub Actions, and Watson ML
• Monitoring with IBM OpenScale
• Project workflow

Tools Discussed

The guide centers on a specific toolchain, presented with each tool's function and developer:

Because the guide is modular, these are starting points rather than requirements — a team can substitute tools where project needs differ.

The End-to-End Example

The guide includes a complete example project that demonstrates the full MLOps workflow, and a video walkthrough of that workflow. The architecture diagram shows the complete MLOps flow used in the tutorial. The example project is linked from the guide and is described as an end-to-end example made specifically for it.

How to Decide Where to Start

• New to MLOps concepts: start with "What is MLOps?" and the Principles section before touching any tooling.
• Ready to build: go to the Implementation Guide, beginning with the Introduction on tools and project structure.
• Setting up infrastructure: the Environment section (IAM access, Terraform, deployment space) is the prerequisite for deployment and CI/CD.
• Already have versioning and deployment: jump to CI/CD for machine learning and Monitoring with IBM OpenScale.
• Want to see the whole picture first: review the architecture diagram and the end-to-end example project before choosing a starting point.

The guide's own "Next" link and table of contents are the most reliable way to move through the material in order.