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https://github.com/pythondeveloper6/awesome-mlops

All the available resources to master MLOPS from scratch
https://github.com/pythondeveloper6/awesome-mlops

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automl aws-sagemaker azure-ml bentoml dagshub django docker dvc fastapi git gradio kubeflow mlflow mlops pycaret shape streamlit zenml

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All the available resources to master MLOPS from scratch

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README 

        
# Awesome MLOPS

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



  

![](logo.png)



## Table of Contents



- [Awesome MLOPS](#awesome-mlops)

  - [Table of Contents](#table-of-contents)

  - [Introduction](#introduction)

  - [Roadmaps](#roadmaps)

  - [One Video](#one-video)

  - [Playlists](#playlists)

  - [Youtube channels](#youtube-channels)

  - [Linkedin Accounts](#linkedin-accounts)

  - [Books](#books)

  - [Blogs](#blogs)

  - [Free Courses](#free-courses)

  - [Paid Courses](#paid-courses)

  - [Communities](#communities)

  - [Projects](#projects)

  - [Tools](#tools)

  - [Contributing](#contributing)



## 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.



- [What is MLOps? | AWS](https://aws.amazon.com/what-is/mlops/)

- [What is MLOps? | Nvidia](https://blogs.nvidia.com/blog/what-is-mlops/)

- [What is MLOps? | Ubuntu](https://ubuntu.com/blog/what-is-mlops)

- [Why Should You Use MLOps? | AWS](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-projects-why.html)

- [Introduction to MLOps](https://blog.paperspace.com/introduction-to-mlops/)

- [MLOps and the evolution of data science | IBM](https://www.ibm.com/blog/mlops-and-the-evolution-of-data-science/)

- [MLOps: Enabling Operationalization of ML at Scale](https://www.iguazio.com/mlops/)



## Roadmaps



- [Complete RoadMap To Learn AIOPS or MLOPS](https://www.youtube.com/watch?v=f8AtpzmRzFc)

- [MLOps Roadmap | Secure Top Jobs Instantly](https://www.youtube.com/watch?v=RebzueX3Ih8)

- [MLOps Roadmap 2024 | MLOps Career Path 2024 | MLOps Careers | Simplilearn](https://www.youtube.com/watch?v=xP_OPVt0Mr0)

- [MLOps Explained | MLOps Roadmap | Future of Data & AI](https://www.youtube.com/watch?v=1JRcYSoyp8E)

- [What is MLOps and how to get started? | MLOps series](https://www.youtube.com/watch?v=LdLFJUlPa4Y)



## One Video



- [MLOps Full Course | MLOps Tutorial For Beginners | Machine Learning Operations | Intellipaat](https://www.youtube.com/watch?v=0Z0vZU6cMKY&t=1s)

- [MLOps Course – Build Machine Learning Production Grade Projects](https://www.youtube.com/watch?v=-dJPoLm_gtE)

- [MLOps Roadmap 2024 | MLOps Career Path 2024 | MLOps Careers | Simplilearn](https://www.youtube.com/watch?v=xP_OPVt0Mr0)

- [Enterprise MLOps 101 | Nvidia](https://www.nvidia.com/en-us/on-demand/session/gtcspring23-s51616/)

- [Best Practices to Accelerate ML Workflows and Reduce Computational Debt with MLOps | Nvidia](https://www.nvidia.com/en-us/on-demand/session/gtcfall20-a22204/)

- [Introduction to Machine Learning Operations | Ubuntu](https://www.youtube.com/watch?v=_YjqHJNNnKE)



## Playlists



- [Machine Learning Engineering for Production (MLOps)](https://www.youtube.com/playlist?list=PLkDaE6sCZn6GMoA0wbpJLi3t34Gd8l0aK)

- [MLOps Zoomcamp 2022](https://www.youtube.com/playlist?list=PL3MmuxUbc_hLG1MoGNxJ9DmQSSM2bEdQT)

- [MLOps Tutorials DVCorg](https://www.youtube.com/playlist?list=PL7WG7YrwYcnDBDuCkFbcyjnZQrdskFsBz)

- [MLOps Hands On Implementation](https://www.youtube.com/playlist?list=PLwFaZuSL_mfou923msxLWAqxkj6Zcnt29)

- [MLOPS Krish Naik](https://www.youtube.com/playlist?list=PLZoTAELRMXVOk1pRcOCaG5xtXxgMalpIe)

- [MLOps - Machine Learning Operations](https://www.youtube.com/playlist?list=PL3N9eeOlCrP5a6OA473MA4KnOXWnUyV_J)

- [Azure MLOps - DevOps for Machine Learning MG](https://www.youtube.com/playlist?list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f)



## Youtube channels 



- [MLOpscommunity](https://www.youtube.com/c/MLOpscommunity)

- [Krish Naik](https://www.youtube.com/@krishnaik06)

- [DSwithBappy](https://www.youtube.com/@dswithbappy)

- [MLOps World: Machine Learning in Production](https://www.youtube.com/@mlopsworldmachinelearningi9769)

- [MLOps Learners](https://www.youtube.com/@mlopslearners)

- [DataTalksClub](https://www.youtube.com/@DataTalksClub/videos)

- [AiOps & MLOps School](https://www.youtube.com/@theaiops)

- [Miki Bazeley - The MLOps Engineer](https://www.youtube.com/@Miki_ML)

- [Sokratis Kartakis](https://www.youtube.com/@sokratis.kartakis/videos)

- [MLOps London](https://www.youtube.com/@mlopslondon)



## Linkedin Accounts 

- [Noah Gift](https://www.linkedin.com/in/noahgift/)

- [Youssef Hosni](https://www.linkedin.com/in/youssef-hosni-b2960b135/)

- [Mohammad Oghli](https://www.linkedin.com/in/mohammad-oghli/)

- [Rahul Parundekar](https://www.linkedin.com/in/rparundekar/)

- [MLOps Newsletter](https://www.linkedin.com/newsletters/mlops-newsletter-6968165653283266560/)

- [Paul Iusztin](https://www.linkedin.com/in/pauliusztin/)

- [Himanshu Ramchandani](https://www.linkedin.com/in/hemansnation/)

- [Khuyen Tran](https://www.linkedin.com/in/khuyen-tran-1401/)

- [MLOps Community](https://www.linkedin.com/company/mlopscommunity/)

- [Raphaël Hoogvliets](https://www.linkedin.com/in/hoogvliets/)

- [Patricia Kato](https://www.linkedin.com/in/patriciakato/)

- [Hugo Albuquerque](https://www.linkedin.com/in/hugo-albuquerque-cosme-da-silva/)



## Books

- [What Is MLOps?](https://www.oreilly.com/library/view/what-is-mlops/9781492093626/)

- [Reliable Machine Learning](https://www.oreilly.com/library/view/reliable-machine-learning/9781098106218/)

- [Designing Machine Learning Systems](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/)

- [Implementing MLOps in the Enterprise](https://www.oreilly.com/library/view/implementing-mlops-in/9781098136574/?_gl=1*ev4ki1*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzQ1OS41Ni4wLjA.)

- [MLOps Engineering at Scale](https://www.oreilly.com/library/view/mlops-engineering-at/9781617297762/?_gl=1*ev4ki1*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzQ1OS41Ni4wLjA.)

- [Engineering MLOps](https://www.oreilly.com/library/view/engineering-mlops/9781800562882/?_gl=1*dmg820*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzUzNy42MC4wLjA.)

- [Enterprise MLOps Interviews](https://www.oreilly.com/library/view/enterprise-mlops-interviews/08012022VIDEOPAIML/?_gl=1*19w4nom*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzU0MS42MC4wLjA.)

- [Introducing MLOps: How to Scale Machine Learning in the Enterprise](https://www.amazon.com/Introducing-MLOps-Machine-Learning-Enterprise/dp/1492083291/)



## Blogs

- [Practitioners guide to MLOps | Google](https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf)

- [ML Models Containerization using Docker](https://www.linkedin.com/pulse/ml-models-containerizing-using-docker-mlops-mohammad-oghli-9ss7f/)

- [A guide to MLOps | Ubuntu Whitepaper](https://ubuntu.com/engage/mlops-guide)

- [MLOps Toolkit Explained | Ubuntu Whitepaper](https://ubuntu.com/engage/mlops-toolkit)

- [Google Cloud Platform with ML Pipeline: A Step-to-Step Guide](https://www.analyticsvidhya.com/blog/2022/01/google-cloud-platform/)

- [What is MLflow?](https://canonical.com/blog/what-is-mlflow)

- [Building a comprehensive toolkit for machine learning](https://canonical.com/blog/machine-learning-toolkit)

- [Made With ML](https://madewithml.com)

- [Mlops Community](https://mlops.community/blog/)

- [Valohai](https://valohai.com/blog/)

- [Evidentlyai](https://www.evidentlyai.com/category/mlops)

- [MLOps.community Medium](https://medium.com/mlops-community)

- [The MLOps Blog](https://neptune.ai/blog)

- [DagsHub MLOps](https://dagshub.com/blog/tag/mlops/)

- [Polyaxon](https://polyaxon.com/blog/)

- [360digitmg](https://360digitmg.com/blog-category/mlops)

- [Nimblebox](https://blog.nimblebox.ai)

- [Fiddler](https://www.fiddler.ai/blog-categories/mlops)

- [Nvidia](https://developer.nvidia.com/blog/tag/mlops/)

- [Censius](https://censius.ai/blogs)

- [Arrikto’s MLOps and Kubeflow Blog](https://journal.arrikto.com)

- [ZenML Blog](https://www.zenml.io/blog)

- [Mlops Now](https://mlopsnow.com/blog/)

- [Data Tron](https://datatron.com/blog/)



## Free Courses 



- [MLOps Specialization by DeepLearning.AI](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops)

- [MLOps | Machine Learning Operations Specialization](https://www.coursera.org/specializations/mlops-machine-learning-duke)

- [MLOps Fundamentals by Google Cloud](https://www.coursera.org/learn/mlops-fundamentals?irclickid=xXASI9XEfxyIUUY36z15iWZRUkD2gAyoZ2m5Rg0&irgwc=1&utm_medium=partners&utm_source=impact&utm_campaign=2382270&utm_content=b2c)

- [Effective MLOps: Model Development](https://www.wandb.courses/courses/effective-mlops-model-development)

- [MLOps Fundamentals](https://www.mygreatlearning.com/academy/learn-for-free/courses/mlops-fundamentals)

- [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)

- [MLOps2 (AWS)](https://www.edx.org/learn/amazon-web-services-aws/statistics-com-mlops2-aws-data-pipeline-automation-optimization-using-amazon-web-services)

- [MLOps Concepts](https://www.datacamp.com/courses/mlops-concepts)

- [MLOps Deployment and Life Cycling](https://www.datacamp.com/courses/mlops-deployment-and-life-cycling)



## Paid Courses 



- [Learn MLOps for Machine Learning](https://www.oreilly.com/library/view/learn-mlops-for/9780138204785/?_gl=1*nmfsia*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzUzNy42MC4wLjA.)

- [Introduction to MLflow for MLOps](https://www.oreilly.com/library/view/introduction-to-mlflow/28188975VIDEOPAIML/?_gl=1*1mbqd7v*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzU0OC41My4wLjA.)

- [Hands-on Python for MLOps](https://www.oreilly.com/library/view/hands-on-python-for/28188920VIDEOPAIML/?_gl=1*11ahkm4*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzU2My4zOC4wLjA.)

- [Hugging Face for MLOps](https://www.oreilly.com/library/view/hugging-face-for/28189144VIDEOPAIML/?_gl=1*1mbqd7v*_ga*NzU5Nzc0MzUuMTcwMTUwNjM0MQ..*_ga_092EL089CH*MTcwMTgwNzMxMy4zLjEuMTcwMTgwNzU0OC41My4wLjA.)

- [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.)

- [Master Practical MLOps for Data Scientists & DevOps on AWS](https://www.udemy.com/course/practical-mlops-for-data-scientists-devops-engineers-aws/)

- [MLflow in Action - Master the art of MLOps using MLflow tool](https://www.udemy.com/course/mlflow-course/)

- [Azure Machine Learning & MLOps : Beginner to Advance](https://www.udemy.com/course/azure-machine-learning-mlops-mg/)

- [Deployment of Machine Learning Models](https://www.udemy.com/course/deployment-of-machine-learning-models/)

- [Mastering MLOps: Complete course for ML Operations](https://www.udemy.com/course/mastering-mlops-complete-course-for-ml-operations/)



## Communities



- [MLOps.community on Slack](https://mlops.community)

- [CDF Special Interest Group – MLOps]()



## Projects



- [End To End MLOPS Data Science Project Implementation With Deployment](https://www.youtube.com/watch?v=pxk1Fr33-L4)

- [Best MLOps Practices for Building End-to-End Machine Learning Computer Vision Projects with Alex Kim](https://www.youtube.com/watch?v=E26IaD7bNXg)

- [End To End Deep Learning Project Using MLOPS DVC Pipeline With Deployments Azure And AWS- Krish Naik](https://www.youtube.com/watch?v=p1bfK8ZJgkE)

- [End To End Machine Learning Project Implementation With Dockers,Github Actions And Deployment](https://www.youtube.com/watch?v=MJ1vWb1rGwM)

- [MLOps with Azure - Hands on Session](https://www.youtube.com/watch?v=pLd7xF0z5Zs)

- [MLOPS End To End Implementation From Basics- Machine Learning](https://www.youtube.com/watch?v=n4sz9cG_B7k)

- [Complete End to End Deep Learning Project With MLFLOW,DVC And Deployment](https://www.youtube.com/watch?v=86BKEv0X2xU)

- [Introduction To MLflow | Track Your Machine Learning Experiments | MLOps](https://www.youtube.com/watch?v=ksYIVDue8ak)

- [MLOPs Projects](https://www.youtube.com/watch?v=esqFvJp8fac)

- [MLOPS-Machine Learning Production Grade Deployment Technqiues With MLOPS In One Shot](https://www.youtube.com/watch?v=lDWUJiivMX8)

- [End to end Deep Learning Project Implementation using MLOps Tool MLflow & DVC with CICD Deployment](https://www.youtube.com/watch?v=-NOIWzjJK-4)

- [BentoML | Build Production Grade AI Applications | MLOps](https://www.youtube.com/watch?v=TWMIFi_ON1M)

- [Build CI/CD Pipelines for ML Projects with Azure Devops](https://www.youtube.com/watch?v=xbgMqCuWgzs)

- [MLOPS - Running Successful AI Projects in Production](https://www.youtube.com/watch?v=C79Ut0fVDSY)

- [End-to-End MLOps Project using one component on Azure](https://www.youtube.com/watch?v=Lpi6d-MgJVI)

- [MLOps Tutorial - Building a CI/ CD Machine Learning Pipeline](https://www.youtube.com/watch?v=XoXvX8MyW8M)



## Tools



- [mlflow](https://mlflow.org) - helps you manage core parts of the machine learning lifecycle.

- [dagshub](https://dagshub.com) - a platform made for the machine learning community to track and version the data, models, experiments, ML pipelines, and code

- [docker](https://www.docker.com) - an open platform for developing, shipping, and running applications

- [zenml](https://www.zenml.io) - helps you create MLOps pipelines without the infrastructure complexity

- [Amazon SageMaker](https://aws.amazon.com/pm/sagemaker/?gclid=Cj0KCQiAsburBhCIARIsAExmsu7H_9sFi10FGKt5u_dHd73wamt7EIJWWu0FBo1Q7HygmyYZBLwGTPYaAhQxEALw_wcB&trk=b9fddfb8-9b30-4c54-8f91-ff16fad4dfed&sc_channel=ps&ef_id=Cj0KCQiAsburBhCIARIsAExmsu7H_9sFi10FGKt5u_dHd73wamt7EIJWWu0FBo1Q7HygmyYZBLwGTPYaAhQxEALw_wcB:G:s&s_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.

- [comet](https://www.comet.com/site/) - a platform for tracking, comparing, explaining, and optimizing machine learning models and experiments

- [Weights & Biases](https://wandb.ai/site) - an ML platform for experiment tracking, data and model versioning, hyperparameter optimization, and model management.

- [prefect](https://www.prefect.io) - a modern data stack for monitoring, coordinating, and orchestrating workflows between and across applications

- [metaflow](https://metaflow.org) - a powerful, battle-hardened workflow management tool for data science and machine learning projects

- [kedro](https://kedro.org) -  a workflow orchestration tool based on Python. You can use it for creating reproducible, maintainable, and modular data science projects

- [pachyderm](https://www.pachyderm.com) - automates data transformation with data versioning, lineage, and end-to-end pipelines on Kubernetes.

- [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. 

- [bentoml](https://www.bentoml.com) - makes it easy and faster to ship machine learning applications

- [evidentlyai](https://www.evidentlyai.com) - an open-source Python library for monitoring ML models during development, validation, and in production

- [fiddler](https://www.fiddler.ai) - an ML model monitoring tool with an easy-to-use, clear UI.

- [censius](https://censius.ai) - an end-to-end AI observability platform that offers automatic monitoring and proactive troubleshooting.

- [kubeflow](https://www.kubeflow.org/docs/) - makes machine learning model deployment on Kubernetes simple, portable, and scalable

- [qwak](https://www.qwak.com) - fully-managed, accessible, and reliable ML platform to develop and deploy models and monitor the entire machine learning pipeline

- [datarobot](https://www.datarobot.com/platform/mlops/) - offers features such as automated model deployment, monitoring, and governance

- [valohai](https://valohai.com/product/) - provides a collaborative environment for managing and automating machine learning projects.

- [aimstack](https://aimstack.io) - an open-source AI metadata tracking tool designed to handle thousands of tracked metadata sequences

- [tecton](https://www.tecton.ai/feature-store/) - a feature platform designed to manage the end-to-end lifecycle of features

- [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

- [Paperspace](https://www.paperspace.com/artificial-intelligence) - a platform for building and scaling AI applications

- [Charmed Kubeflow](https://charmed-kubeflow.io/) - 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.