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MLOps Guide

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This site is intended to be a MLOps Guide to help projects and companies to build more reliable MLOps environment. This guide should contemplate the theory behind MLOps and an implementation that should fit for most use cases. Made by Arthur Olga, Gabriel Monteiro, Guilherme Leite and Vinicius Lima

What is MLOps?

MLOps is a methodology of operation that aims to facilitate the process of bringing an experimental Machine Learning model into production and maintaining it efficiently. MLOps focus on bringing the methodology of DevOps used in the software industry to the Machine Learning model lifecycle. In that way we can define some of the main features of a MLOPs project:

  • Data and Model Versioning
  • Feature Management and Storing
  • Automation of Pipelines and Processes
  • CI/CD for Machine Learning
  • Continuous Monitoring of Models

What is Contemplated on This Guide?

  • Introduction to MLOps Concepts
  • Tutorial for Building a MLOps Environment

MLOps Environment

This video shows how an example of workflow with a complete MLOps project. This exact project can be found here and is an example end-to-end made for this guide.


The following diagram shows the complete MLOps flow used on the tutorial. Since the guide is modular, a team can choose to swap tools at any point due to project preferences and use cases.


Project Tools

The main tools discussed in the guide are shown in the following table.

Tools Function Developer License
IBM Watson ML Deploying model as API IBM Proprietary
IBM Watson OpenScale Monitoring Model in production IBM Proprietary
DVC Data and Model Versioning Iterative Apache License 2.0
CML Pipeline Automation Iterative Apache License 2.0
Terraform Setups IBM infrastructure with script HashiCorp Mozilla Public License v2.0
Github Code versioning Github Proprietary
Github Actions CI/CD Automation Github Proprietary
Pytest Python script testing Pytest-dev MIT
Pre-commit Running tests on local commit Pre-commit MIT
Cookiecutter Creating folder structure and files Cookiecutter BSD 3-Clause


MLOps Theory

📚 Learn More About MLOps Theory


It is recommended that you learn about the theory before implementing MLOps into your project

Implementation Guide

📃 Follow the Tutorial to Start a Project