This repository is a curated list of tools and resources for MLOps (Machine Learning Operations). It aims to provide a comprehensive overview of the MLOps landscape, covering everything from data management and feature engineering to model serving and monitoring, serving as a valuable reference for ML engineers, data scientists, and DevOps professionals looking to streamline and operationalize their machine learning workflows.

How It Works

The list is organized into categories that mirror the machine learning lifecycle, such as Data Catalog, CI/CD for Machine Learning, Feature Engineering, Model Serving, and Model Testing & Validation. Each category contains a curated selection of open-source and commercial tools, along with brief descriptions and links to their respective projects, enabling users to discover and evaluate relevant solutions for their MLOps needs.

Quick Start & Requirements

This is a curated list, not a software package. No installation or execution is required.

Highlighted Details

• Comprehensive coverage across all stages of the ML lifecycle.
• Includes a wide range of tools, from foundational data processing to advanced model interpretability.
• Features extensive resources including articles, books, podcasts, and community links.
• Categorizes tools for easy navigation and selection.

Maintenance & Community

The list is inspired by "awesome-python" and welcomes contributions. Links to community resources like Slack channels are provided.

Licensing & Compatibility

This repository is a list of links and does not have a specific license. The licenses of the individual tools listed vary.

Limitations & Caveats

As a curated list, the quality and maintenance of the individual tools are not guaranteed by this repository. The rapidly evolving nature of MLOps means the list may require frequent updates to remain current.