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.
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
Architecture
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.
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| 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 |