A curated list of resources, tools, frameworks, articles, and projects related to Machine Learning Operations (MLOps).
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.
- What is MLOps? | AWS
- What is MLOps? | Nvidia
- What is MLOps? | Ubuntu
- Why Should You Use MLOps? | AWS
- Introduction to MLOps
- MLOps and the evolution of data science | IBM
- MLOps: Enabling Operationalization of ML at Scale
- Complete RoadMap To Learn AIOPS or MLOPS
- MLOps Roadmap | Secure Top Jobs Instantly
- MLOps Roadmap 2024 | MLOps Career Path 2024 | MLOps Careers | Simplilearn
- MLOps Explained | MLOps Roadmap | Future of Data & AI
- What is MLOps and how to get started? | MLOps series
- MLOps Full Course | MLOps Tutorial For Beginners | Machine Learning Operations | Intellipaat
- MLOps Course – Build Machine Learning Production Grade Projects
- MLOps Roadmap 2024 | MLOps Career Path 2024 | MLOps Careers | Simplilearn
- Enterprise MLOps 101 | Nvidia
- Best Practices to Accelerate ML Workflows and Reduce Computational Debt with MLOps | Nvidia
- Introduction to Machine Learning Operations | Ubuntu
- Machine Learning Engineering for Production (MLOps)
- MLOps Zoomcamp 2022
- MLOps Tutorials DVCorg
- MLOps Hands On Implementation
- MLOPS Krish Naik
- MLOps - Machine Learning Operations
- Azure MLOps - DevOps for Machine Learning MG
- MLOpscommunity
- Krish Naik
- DSwithBappy
- MLOps World: Machine Learning in Production
- MLOps Learners
- DataTalksClub
- AiOps & MLOps School
- Miki Bazeley - The MLOps Engineer
- Sokratis Kartakis
- MLOps London
- Noah Gift
- Youssef Hosni
- Mohammad Oghli
- Rahul Parundekar
- MLOps Newsletter
- Paul Iusztin
- Himanshu Ramchandani
- Khuyen Tran
- MLOps Community
- Raphaël Hoogvliets
- Patricia Kato
- Hugo Albuquerque
- What Is MLOps?
- Reliable Machine Learning
- Designing Machine Learning Systems
- Implementing MLOps in the Enterprise
- MLOps Engineering at Scale
- Engineering MLOps
- Enterprise MLOps Interviews
- Introducing MLOps: How to Scale Machine Learning in the Enterprise
- Practitioners guide to MLOps | Google
- ML Models Containerization using Docker
- A guide to MLOps | Ubuntu Whitepaper
- MLOps Toolkit Explained | Ubuntu Whitepaper
- Google Cloud Platform with ML Pipeline: A Step-to-Step Guide
- What is MLflow?
- Building a comprehensive toolkit for machine learning
- Made With ML
- Mlops Community
- Valohai
- Evidentlyai
- MLOps.community Medium
- The MLOps Blog
- DagsHub MLOps
- Polyaxon
- 360digitmg
- Nimblebox
- Fiddler
- Nvidia
- Censius
- Arrikto’s MLOps and Kubeflow Blog
- ZenML Blog
- Mlops Now
- Data Tron
- MLOps Specialization by DeepLearning.AI
- MLOps | Machine Learning Operations Specialization
- MLOps Fundamentals by Google Cloud
- Effective MLOps: Model Development
- MLOps Fundamentals
- MLOps1 (AWS)
- MLOps2 (AWS)
- MLOps Concepts
- MLOps Deployment and Life Cycling
- Learn MLOps for Machine Learning
- Introduction to MLflow for MLOps
- Hands-on Python for MLOps
- Hugging Face for MLOps
- Doing MLOps with Databricks and MLFlow - Full Course
- Master Practical MLOps for Data Scientists & DevOps on AWS
- MLflow in Action - Master the art of MLOps using MLflow tool
- Azure Machine Learning & MLOps : Beginner to Advance
- Deployment of Machine Learning Models
- Mastering MLOps: Complete course for ML Operations
- End To End MLOPS Data Science Project Implementation With Deployment
- Best MLOps Practices for Building End-to-End Machine Learning Computer Vision Projects with Alex Kim
- End To End Deep Learning Project Using MLOPS DVC Pipeline With Deployments Azure And AWS- Krish Naik
- End To End Machine Learning Project Implementation With Dockers,Github Actions And Deployment
- MLOps with Azure - Hands on Session
- MLOPS End To End Implementation From Basics- Machine Learning
- Complete End to End Deep Learning Project With MLFLOW,DVC And Deployment
- Introduction To MLflow | Track Your Machine Learning Experiments | MLOps
- MLOPs Projects
- MLOPS-Machine Learning Production Grade Deployment Technqiues With MLOPS In One Shot
- End to end Deep Learning Project Implementation using MLOps Tool MLflow & DVC with CICD Deployment
- BentoML | Build Production Grade AI Applications | MLOps
- Build CI/CD Pipelines for ML Projects with Azure Devops
- MLOPS - Running Successful AI Projects in Production
- End-to-End MLOps Project using one component on Azure
- MLOps Tutorial - Building a CI/ CD Machine Learning Pipeline
- 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
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.
