MLOps Guide by Arthur Olga, Gabriel Monteiro, Guilherme Leite and Vinicius Lima

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.

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 does this guide cover?
- Introduction to MLOps Concepts
- Tutorial for Building a MLOps Environment

Link: Direct

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