{"id":78996,"url":"https://github.com/pythondeveloper6/awesome-mlops","name":"awesome-mlops","description":"All the available resources to master MLOPS from scratch ","projects_count":140,"last_synced_at":"2026-08-01T06:00:33.741Z","repository":{"id":211050676,"uuid":"699820882","full_name":"Pythondeveloper6/Awesome-MLOPS","owner":"Pythondeveloper6","description":"All the available resources to master MLOPS from scratch ","archived":false,"fork":false,"pushed_at":"2024-05-07T18:59:34.000Z","size":62,"stargazers_count":379,"open_issues_count":4,"forks_count":64,"subscribers_count":5,"default_branch":"main","last_synced_at":"2026-06-24T21:02:42.894Z","etag":null,"topics":["automl","aws-sagemaker","azure-ml","bentoml","dagshub","django","docker","dvc","fastapi","git","gradio","kubeflow","mlflow","mlops","pycaret","shape","streamlit","zenml"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Pythondeveloper6.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-10-03T12:09:52.000Z","updated_at":"2026-06-22T18:12:25.000Z","dependencies_parsed_at":"2024-11-18T14:10:16.565Z","dependency_job_id":"322d6d05-fa63-471e-a90d-ce4decc871ff","html_url":"https://github.com/Pythondeveloper6/Awesome-MLOPS","commit_stats":null,"previous_names":["pythondeveloper6/awesome-mlops"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Pythondeveloper6/Awesome-MLOPS","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pythondeveloper6%2FAwesome-MLOPS","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pythondeveloper6%2FAwesome-MLOPS/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pythondeveloper6%2FAwesome-MLOPS/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pythondeveloper6%2FAwesome-MLOPS/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Pythondeveloper6","download_url":"https://codeload.github.com/Pythondeveloper6/Awesome-MLOPS/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pythondeveloper6%2FAwesome-MLOPS/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35408466,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-13T02:00:06.543Z","response_time":119,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-11-18T14:09:48.415Z","updated_at":"2026-08-01T06:00:33.742Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Tools","Introduction","Books","Free Courses","Blogs","Roadmaps","One Video","Playlists","Youtube channels","Linkedin Accounts","Paid Courses","Projects","Communities"],"sub_categories":[],"readme":"# Awesome MLOPS\nA curated list of resources, tools, frameworks, articles, and projects related to Machine Learning Operations (MLOps).\n\n  \n![](logo.png)\n\n\n## Table of Contents\n\n\n- [Awesome MLOPS](#awesome-mlops)\n  - [Table of Contents](#table-of-contents)\n  - [Introduction](#introduction)\n  - [Roadmaps](#roadmaps)\n  - [One Video](#one-video)\n  - [Playlists](#playlists)\n  - [Youtube channels](#youtube-channels)\n  - [Linkedin Accounts](#linkedin-accounts)\n  - [Books](#books)\n  - [Blogs](#blogs)\n  - [Free Courses](#free-courses)\n  - [Paid Courses](#paid-courses)\n  - [Communities](#communities)\n  - [Projects](#projects)\n  - [Tools](#tools)\n  - [Contributing](#contributing)\n\n## Introduction\n\nWelcome 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.\n\n- [What is MLOps? | AWS](https://aws.amazon.com/what-is/mlops/)\n- [What is MLOps? | Nvidia](https://blogs.nvidia.com/blog/what-is-mlops/)\n- [What is MLOps? | Ubuntu](https://ubuntu.com/blog/what-is-mlops)\n- [Why Should You Use MLOps? | AWS](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-projects-why.html)\n- [Introduction to MLOps](https://blog.paperspace.com/introduction-to-mlops/)\n- [MLOps and the evolution of data science | IBM](https://www.ibm.com/blog/mlops-and-the-evolution-of-data-science/)\n- [MLOps: Enabling Operationalization of ML at Scale](https://www.iguazio.com/mlops/)\n\n## Roadmaps\n\n- [Complete RoadMap To Learn AIOPS or MLOPS](https://www.youtube.com/watch?v=f8AtpzmRzFc)\n- [MLOps Roadmap | Secure Top Jobs Instantly](https://www.youtube.com/watch?v=RebzueX3Ih8)\n- [MLOps Roadmap 2024 | MLOps Career Path 2024 | MLOps Careers | Simplilearn](https://www.youtube.com/watch?v=xP_OPVt0Mr0)\n- [MLOps Explained | MLOps Roadmap | Future of Data \u0026 AI](https://www.youtube.com/watch?v=1JRcYSoyp8E)\n- [What is MLOps and how to get started? | MLOps series](https://www.youtube.com/watch?v=LdLFJUlPa4Y)\n\n\n## One Video\n\n- [MLOps Full Course | MLOps Tutorial For Beginners | Machine Learning Operations | Intellipaat](https://www.youtube.com/watch?v=0Z0vZU6cMKY\u0026t=1s)\n- [MLOps Course – Build Machine Learning Production Grade Projects](https://www.youtube.com/watch?v=-dJPoLm_gtE)\n- [MLOps Roadmap 2024 | MLOps Career Path 2024 | MLOps Careers | Simplilearn](https://www.youtube.com/watch?v=xP_OPVt0Mr0)\n- [Enterprise MLOps 101 | Nvidia](https://www.nvidia.com/en-us/on-demand/session/gtcspring23-s51616/)\n- [Best Practices to Accelerate ML Workflows and Reduce Computational Debt with MLOps | Nvidia](https://www.nvidia.com/en-us/on-demand/session/gtcfall20-a22204/)\n- [Introduction to Machine Learning Operations | Ubuntu](https://www.youtube.com/watch?v=_YjqHJNNnKE)\n\n\n\n\n## Playlists\n\n- [Machine Learning Engineering for Production (MLOps)](https://www.youtube.com/playlist?list=PLkDaE6sCZn6GMoA0wbpJLi3t34Gd8l0aK)\n- [MLOps Zoomcamp 2022](https://www.youtube.com/playlist?list=PL3MmuxUbc_hLG1MoGNxJ9DmQSSM2bEdQT)\n- [MLOps Tutorials DVCorg](https://www.youtube.com/playlist?list=PL7WG7YrwYcnDBDuCkFbcyjnZQrdskFsBz)\n- [MLOps Hands On Implementation](https://www.youtube.com/playlist?list=PLwFaZuSL_mfou923msxLWAqxkj6Zcnt29)\n- [MLOPS Krish Naik](https://www.youtube.com/playlist?list=PLZoTAELRMXVOk1pRcOCaG5xtXxgMalpIe)\n- [MLOps - Machine Learning Operations](https://www.youtube.com/playlist?list=PL3N9eeOlCrP5a6OA473MA4KnOXWnUyV_J)\n- [Azure MLOps - DevOps for Machine Learning MG](https://www.youtube.com/playlist?list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f)\n\n## Youtube channels \n\n- [MLOpscommunity](https://www.youtube.com/c/MLOpscommunity)\n- [Krish Naik](https://www.youtube.com/@krishnaik06)\n- [DSwithBappy](https://www.youtube.com/@dswithbappy)\n- [MLOps World: Machine Learning in Production](https://www.youtube.com/@mlopsworldmachinelearningi9769)\n- [MLOps Learners](https://www.youtube.com/@mlopslearners)\n- [DataTalksClub](https://www.youtube.com/@DataTalksClub/videos)\n- [AiOps \u0026 MLOps School](https://www.youtube.com/@theaiops)\n- [Miki Bazeley - The MLOps Engineer](https://www.youtube.com/@Miki_ML)\n- [Sokratis Kartakis](https://www.youtube.com/@sokratis.kartakis/videos)\n- [MLOps London](https://www.youtube.com/@mlopslondon)\n\n\n## Linkedin Accounts \n- [Noah Gift](https://www.linkedin.com/in/noahgift/)\n- [Youssef Hosni](https://www.linkedin.com/in/youssef-hosni-b2960b135/)\n- [Mohammad Oghli](https://www.linkedin.com/in/mohammad-oghli/)\n- [Rahul Parundekar](https://www.linkedin.com/in/rparundekar/)\n- [MLOps Newsletter](https://www.linkedin.com/newsletters/mlops-newsletter-6968165653283266560/)\n- [Paul Iusztin](https://www.linkedin.com/in/pauliusztin/)\n- [Himanshu Ramchandani](https://www.linkedin.com/in/hemansnation/)\n- [Khuyen Tran](https://www.linkedin.com/in/khuyen-tran-1401/)\n- [MLOps Community](https://www.linkedin.com/company/mlopscommunity/)\n- [Raphaël Hoogvliets](https://www.linkedin.com/in/hoogvliets/)\n- [Patricia Kato](https://www.linkedin.com/in/patriciakato/)\n- [Hugo Albuquerque](https://www.linkedin.com/in/hugo-albuquerque-cosme-da-silva/)\n\n\n\n## Books\n- [What Is MLOps?](https://www.oreilly.com/library/view/what-is-mlops/9781492093626/)\n- [Reliable Machine Learning](https://www.oreilly.com/library/view/reliable-machine-learning/9781098106218/)\n- [Designing Machine Learning Systems](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/)\n- [Implementing MLOps in the Enterprise](https://www.oreilly.com/library/view/implementing-mlops-in/9781098136574/?_gl=1*ev4ki1*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzQ1OS41Ni4wLjA.)\n- [MLOps Engineering at Scale](https://www.oreilly.com/library/view/mlops-engineering-at/9781617297762/?_gl=1*ev4ki1*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzQ1OS41Ni4wLjA.)\n- [Engineering MLOps](https://www.oreilly.com/library/view/engineering-mlops/9781800562882/?_gl=1*dmg820*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzUzNy42MC4wLjA.)\n- [Enterprise MLOps Interviews](https://www.oreilly.com/library/view/enterprise-mlops-interviews/08012022VIDEOPAIML/?_gl=1*19w4nom*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzU0MS42MC4wLjA.)\n- [Introducing MLOps: How to Scale Machine Learning in the Enterprise](https://www.amazon.com/Introducing-MLOps-Machine-Learning-Enterprise/dp/1492083291/)\n\n\n\n## Blogs\n- [Practitioners guide to MLOps | Google](https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf)\n- [ML Models Containerization using Docker](https://www.linkedin.com/pulse/ml-models-containerizing-using-docker-mlops-mohammad-oghli-9ss7f/)\n- [A guide to MLOps | Ubuntu Whitepaper](https://ubuntu.com/engage/mlops-guide)\n- [MLOps Toolkit Explained | Ubuntu Whitepaper](https://ubuntu.com/engage/mlops-toolkit)\n- [Google Cloud Platform with ML Pipeline: A Step-to-Step Guide](https://www.analyticsvidhya.com/blog/2022/01/google-cloud-platform/)\n- [What is MLflow?](https://canonical.com/blog/what-is-mlflow)\n- [Building a comprehensive toolkit for machine learning](https://canonical.com/blog/machine-learning-toolkit)\n- [Made With ML](https://madewithml.com)\n- [Mlops Community](https://mlops.community/blog/)\n- [Valohai](https://valohai.com/blog/)\n- [Evidentlyai](https://www.evidentlyai.com/category/mlops)\n- [MLOps.community Medium](https://medium.com/mlops-community)\n- [The MLOps Blog](https://neptune.ai/blog)\n- [DagsHub MLOps](https://dagshub.com/blog/tag/mlops/)\n- [Polyaxon](https://polyaxon.com/blog/)\n- [360digitmg](https://360digitmg.com/blog-category/mlops)\n- [Nimblebox](https://blog.nimblebox.ai)\n- [Fiddler](https://www.fiddler.ai/blog-categories/mlops)\n- [Nvidia](https://developer.nvidia.com/blog/tag/mlops/)\n- [Censius](https://censius.ai/blogs)\n- [Arrikto’s MLOps and Kubeflow Blog](https://journal.arrikto.com)\n- [ZenML Blog](https://www.zenml.io/blog)\n- [Mlops Now](https://mlopsnow.com/blog/)\n- [Data Tron](https://datatron.com/blog/)\n\n\n## Free Courses \n\n- [MLOps Specialization by DeepLearning.AI](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops)\n- [MLOps | Machine Learning Operations Specialization](https://www.coursera.org/specializations/mlops-machine-learning-duke)\n- [MLOps Fundamentals by Google Cloud](https://www.coursera.org/learn/mlops-fundamentals?irclickid=xXASI9XEfxyIUUY36z15iWZRUkD2gAyoZ2m5Rg0\u0026irgwc=1\u0026utm_medium=partners\u0026utm_source=impact\u0026utm_campaign=2382270\u0026utm_content=b2c)\n- [Effective MLOps: Model Development](https://www.wandb.courses/courses/effective-mlops-model-development)\n- [MLOps Fundamentals](https://www.mygreatlearning.com/academy/learn-for-free/courses/mlops-fundamentals)\n- [MLOps1 (AWS)](https://www.edx.org/learn/amazon-web-services-aws/statistics-com-mlops1-aws-deploying-ai-ml-models-in-production-using-amazon-web-services)\n- [MLOps2 (AWS)](https://www.edx.org/learn/amazon-web-services-aws/statistics-com-mlops2-aws-data-pipeline-automation-optimization-using-amazon-web-services)\n- [MLOps Concepts](https://www.datacamp.com/courses/mlops-concepts)\n- [MLOps Deployment and Life Cycling](https://www.datacamp.com/courses/mlops-deployment-and-life-cycling)\n\n\n\n\n\n## Paid Courses \n\n- [Learn MLOps for Machine Learning](https://www.oreilly.com/library/view/learn-mlops-for/9780138204785/?_gl=1*nmfsia*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzUzNy42MC4wLjA.)\n- [Introduction to MLflow for MLOps](https://www.oreilly.com/library/view/introduction-to-mlflow/28188975VIDEOPAIML/?_gl=1*1mbqd7v*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzU0OC41My4wLjA.)\n- [Hands-on Python for MLOps](https://www.oreilly.com/library/view/hands-on-python-for/28188920VIDEOPAIML/?_gl=1*11ahkm4*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzU2My4zOC4wLjA.)\n- [Hugging Face for MLOps](https://www.oreilly.com/library/view/hugging-face-for/28189144VIDEOPAIML/?_gl=1*1mbqd7v*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzU0OC41My4wLjA.)\n- [Doing MLOps with Databricks and MLFlow - Full Course](https://www.oreilly.com/library/view/doing-mlops-with/062592022VIDEOPAIML/?_gl=1*1mbqd7v*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzU0OC41My4wLjA.)\n- [Master Practical MLOps for Data Scientists \u0026 DevOps on AWS](https://www.udemy.com/course/practical-mlops-for-data-scientists-devops-engineers-aws/)\n- [MLflow in Action - Master the art of MLOps using MLflow tool](https://www.udemy.com/course/mlflow-course/)\n- [Azure Machine Learning \u0026 MLOps : Beginner to Advance](https://www.udemy.com/course/azure-machine-learning-mlops-mg/)\n- [Deployment of Machine Learning Models](https://www.udemy.com/course/deployment-of-machine-learning-models/)\n- [Mastering MLOps: Complete course for ML Operations](https://www.udemy.com/course/mastering-mlops-complete-course-for-ml-operations/)\n\n\n## Communities\n\n- [MLOps.community on Slack](https://mlops.community)\n- [CDF Special Interest Group – MLOps]()\n\n\n\n\n## Projects\n\n- [End To End MLOPS Data Science Project Implementation With Deployment](https://www.youtube.com/watch?v=pxk1Fr33-L4)\n- [Best MLOps Practices for Building End-to-End Machine Learning Computer Vision Projects with Alex Kim](https://www.youtube.com/watch?v=E26IaD7bNXg)\n- [End To End Deep Learning Project Using MLOPS DVC Pipeline With Deployments Azure And AWS- Krish Naik](https://www.youtube.com/watch?v=p1bfK8ZJgkE)\n- [End To End Machine Learning Project Implementation With Dockers,Github Actions And Deployment](https://www.youtube.com/watch?v=MJ1vWb1rGwM)\n- [MLOps with Azure - Hands on Session](https://www.youtube.com/watch?v=pLd7xF0z5Zs)\n- [MLOPS End To End Implementation From Basics- Machine Learning](https://www.youtube.com/watch?v=n4sz9cG_B7k)\n- [Complete End to End Deep Learning Project With MLFLOW,DVC And Deployment](https://www.youtube.com/watch?v=86BKEv0X2xU)\n- [Introduction To MLflow | Track Your Machine Learning Experiments | MLOps](https://www.youtube.com/watch?v=ksYIVDue8ak)\n- [MLOPs Projects](https://www.youtube.com/watch?v=esqFvJp8fac)\n- [MLOPS-Machine Learning Production Grade Deployment Technqiues With MLOPS In One Shot](https://www.youtube.com/watch?v=lDWUJiivMX8)\n- [End to end Deep Learning Project Implementation using MLOps Tool MLflow \u0026 DVC with CICD Deployment](https://www.youtube.com/watch?v=-NOIWzjJK-4)\n- [BentoML | Build Production Grade AI Applications | MLOps](https://www.youtube.com/watch?v=TWMIFi_ON1M)\n- [Build CI/CD Pipelines for ML Projects with Azure Devops](https://www.youtube.com/watch?v=xbgMqCuWgzs)\n- [MLOPS - Running Successful AI Projects in Production](https://www.youtube.com/watch?v=C79Ut0fVDSY)\n- [End-to-End MLOps Project using one component on Azure](https://www.youtube.com/watch?v=Lpi6d-MgJVI)\n- [MLOps Tutorial - Building a CI/ CD Machine Learning Pipeline](https://www.youtube.com/watch?v=XoXvX8MyW8M)\n\n\n## Tools\n\n- [mlflow](https://mlflow.org) - helps you manage core parts of the machine learning lifecycle.\n- [dagshub](https://dagshub.com) - a platform made for the machine learning community to track and version the data, models, experiments, ML pipelines, and code\n- [docker](https://www.docker.com) - an open platform for developing, shipping, and running applications\n- [zenml](https://www.zenml.io) - helps you create MLOps pipelines without the infrastructure complexity\n- [Amazon SageMaker](https://aws.amazon.com/pm/sagemaker/?gclid=Cj0KCQiAsburBhCIARIsAExmsu7H_9sFi10FGKt5u_dHd73wamt7EIJWWu0FBo1Q7HygmyYZBLwGTPYaAhQxEALw_wcB\u0026trk=b9fddfb8-9b30-4c54-8f91-ff16fad4dfed\u0026sc_channel=ps\u0026ef_id=Cj0KCQiAsburBhCIARIsAExmsu7H_9sFi10FGKt5u_dHd73wamt7EIJWWu0FBo1Q7HygmyYZBLwGTPYaAhQxEALw_wcB:G:s\u0026s_kwcid=AL!4422!3!645208943671!e!!g!!amazon%20sagemaker!19572078909!144705028745) - 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.\n- [comet](https://www.comet.com/site/) - a platform for tracking, comparing, explaining, and optimizing machine learning models and experiments\n- [Weights \u0026 Biases](https://wandb.ai/site) - an ML platform for experiment tracking, data and model versioning, hyperparameter optimization, and model management.\n- [prefect](https://www.prefect.io) - a modern data stack for monitoring, coordinating, and orchestrating workflows between and across applications\n- [metaflow](https://metaflow.org) - a powerful, battle-hardened workflow management tool for data science and machine learning projects\n- [kedro](https://kedro.org) -  a workflow orchestration tool based on Python. You can use it for creating reproducible, maintainable, and modular data science projects\n- [pachyderm](https://www.pachyderm.com) - automates data transformation with data versioning, lineage, and end-to-end pipelines on Kubernetes.\n- [dvc](https://dvc.org) - an open-source tool for machine learning projects. It works seamlessly with Git to provide you with code, data, model, metadata, and pipeline versioning. \n- [bentoml](https://www.bentoml.com) - makes it easy and faster to ship machine learning applications\n- [evidentlyai](https://www.evidentlyai.com) - an open-source Python library for monitoring ML models during development, validation, and in production\n- [fiddler](https://www.fiddler.ai) - an ML model monitoring tool with an easy-to-use, clear UI.\n- [censius](https://censius.ai) - an end-to-end AI observability platform that offers automatic monitoring and proactive troubleshooting.\n- [kubeflow](https://www.kubeflow.org/docs/) - makes machine learning model deployment on Kubernetes simple, portable, and scalable\n- [qwak](https://www.qwak.com) - fully-managed, accessible, and reliable ML platform to develop and deploy models and monitor the entire machine learning pipeline\n- [datarobot](https://www.datarobot.com/platform/mlops/) - offers features such as automated model deployment, monitoring, and governance\n- [valohai](https://valohai.com/product/) - provides a collaborative environment for managing and automating machine learning projects.\n- [aimstack](https://aimstack.io) - an open-source AI metadata tracking tool designed to handle thousands of tracked metadata sequences\n- [tecton](https://www.tecton.ai/feature-store/) - a feature platform designed to manage the end-to-end lifecycle of features\n- [feast](https://github.com/feast-dev/feast) - an open-source feature store with a centralized and scalable platform for managing, serving, and discovering features in MLOps workflows\n- [Paperspace](https://www.paperspace.com/artificial-intelligence) - a platform for building and scaling AI applications\n- [Charmed Kubeflow](https://charmed-kubeflow.io/) - The fully supported MLOps platform for any cloud\n\n\n\n## Contributing\n\nContributions 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.\n\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/pythondeveloper6%2Fawesome-mlops/projects"}