Pythondeveloper6 / Awesome-MLOPS

All the available resources to master MLOPS from scratch

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Awesome MLOPS

A curated list of resources, tools, frameworks, articles, and projects related to Machine Learning Operations (MLOps).

Table of Contents

Introduction

Welcome to Awesome MLOps! This repository aims to gather the best resources related to MLOps, covering a wide range of topics including best practices, tools, frameworks, articles, and projects in the field of Machine Learning Operations.

Roadmaps

One Video

Playlists

Youtube channels

Linkedin Accounts

Books

Blogs

Free Courses

Paid Courses

Communities

Projects

Tools

  • mlflow - helps you manage core parts of the machine learning lifecycle.
  • dagshub - a platform made for the machine learning community to track and version the data, models, experiments, ML pipelines, and code
  • docker - an open platform for developing, shipping, and running applications
  • zenml - helps you create MLOps pipelines without the infrastructure complexity
  • Amazon SageMaker - one solution for MLOps. You can train and accelerate model development, track and version experiments, catalog ML artifacts, integrate CI/CD ML pipelines, and deploy, serve, and monitor models in production seamlessly.
  • comet - a platform for tracking, comparing, explaining, and optimizing machine learning models and experiments
  • Weights & Biases - an ML platform for experiment tracking, data and model versioning, hyperparameter optimization, and model management.
  • prefect - a modern data stack for monitoring, coordinating, and orchestrating workflows between and across applications
  • metaflow - a powerful, battle-hardened workflow management tool for data science and machine learning projects
  • kedro - a workflow orchestration tool based on Python. You can use it for creating reproducible, maintainable, and modular data science projects
  • pachyderm - automates data transformation with data versioning, lineage, and end-to-end pipelines on Kubernetes.
  • dvc - an open-source tool for machine learning projects. It works seamlessly with Git to provide you with code, data, model, metadata, and pipeline versioning.
  • bentoml - makes it easy and faster to ship machine learning applications
  • evidentlyai - an open-source Python library for monitoring ML models during development, validation, and in production
  • fiddler - an ML model monitoring tool with an easy-to-use, clear UI.
  • censius - an end-to-end AI observability platform that offers automatic monitoring and proactive troubleshooting.
  • kubeflow - makes machine learning model deployment on Kubernetes simple, portable, and scalable
  • qwak - fully-managed, accessible, and reliable ML platform to develop and deploy models and monitor the entire machine learning pipeline
  • datarobot - offers features such as automated model deployment, monitoring, and governance
  • valohai - provides a collaborative environment for managing and automating machine learning projects.
  • aimstack - an open-source AI metadata tracking tool designed to handle thousands of tracked metadata sequences
  • tecton - a feature platform designed to manage the end-to-end lifecycle of features
  • feast - an open-source feature store with a centralized and scalable platform for managing, serving, and discovering features in MLOps workflows
  • Paperspace - a platform for building and scaling AI applications
  • Charmed Kubeflow - The fully supported MLOps platform for any cloud

Contributing

Contributions are welcome! If you have resources, tools, frameworks, articles, or projects related to MLOps that you'd like to add, please open a pull request.