awesome-mlops
All the available resources to master MLOPS from scratch
https://github.com/pythondeveloper6/awesome-mlops
Last synced: 3 days ago
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Blogs
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- 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
- 360digitmg
- Nimblebox
- Fiddler
- Nvidia
- Censius
- Arrikto’s MLOps and Kubeflow Blog
- ZenML Blog
- Mlops Now
- Data Tron
- 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
- Mlops Community
- Valohai
- Evidentlyai
- MLOps.community Medium
- The MLOps Blog
- DagsHub MLOps
- Polyaxon
- 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
- Evidentlyai
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Nimblebox
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
- Arrikto’s MLOps and Kubeflow Blog
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Books
- Introducing MLOps: How to Scale Machine Learning in the Enterprise
- 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
- 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
- Enterprise MLOps Interviews
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Tools
- qwak - fully-managed, accessible, and reliable ML platform to develop and deploy models and monitor the entire machine learning pipeline
- 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.
- Charmed Kubeflow - The fully supported MLOps platform for any cloud
- 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
- 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
- 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
- valohai - provides a collaborative environment for managing and automating machine learning projects.
- 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.
- 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
- valohai - provides a collaborative environment for managing and automating machine learning projects.
- 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
- kedro - a workflow orchestration tool based on Python. You can use it for creating reproducible, maintainable, and modular data science projects
- dagshub - a platform made for the machine learning community to track and version the data, models, experiments, ML pipelines, and code
- pachyderm - automates data transformation with data versioning, lineage, and end-to-end pipelines on Kubernetes.
- censius - an end-to-end AI observability platform that offers automatic monitoring and proactive troubleshooting.
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Playlists
- MLOps - Machine Learning Operations
- Azure MLOps - DevOps for Machine Learning MG
- MLOps Hands On Implementation
- MLOPS Krish Naik
- Machine Learning Engineering for Production (MLOps)
- MLOps Zoomcamp 2022
- Machine Learning Engineering for Production (MLOps)
- MLOps Zoomcamp 2022
- MLOps Tutorials DVCorg
- MLOps Hands On Implementation
- MLOPS Krish Naik
- Azure MLOps - DevOps for Machine Learning MG
- MLOps - Machine Learning Operations
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Linkedin Accounts
- Khuyen Tran
- MLOps Community
- Raphaël Hoogvliets
- Patricia Kato
- Hugo Albuquerque
- Rahul Parundekar
- MLOps Newsletter
- Paul Iusztin
- Noah Gift
- Youssef Hosni
- Mohammad Oghli
- Rahul Parundekar
- MLOps Newsletter
- Paul Iusztin
- Himanshu Ramchandani
- Khuyen Tran
- MLOps Community
- Noah Gift
- Youssef Hosni
- Mohammad Oghli
- Raphaël Hoogvliets
- Patricia Kato
- Hugo Albuquerque
- Youssef Hosni
- Mohammad Oghli
- Paul Iusztin
- Himanshu Ramchandani
- Khuyen Tran
- Raphaël Hoogvliets
- Rahul Parundekar
- Noah Gift
- Hugo Albuquerque
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Free Courses
- MLOps Concepts
- MLOps Deployment and Life Cycling
- MLOps Fundamentals by Google Cloud
- Effective MLOps: Model Development
- MLOps Fundamentals
- MLOps1 (AWS)
- MLOps2 (AWS)
- MLOps | Machine Learning Operations Specialization
- 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
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Paid Courses
- 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
- 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
- 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
- Introduction to MLflow for MLOps
- Learn MLOps for Machine Learning
- Hands-on Python for MLOps
- Hugging Face for MLOps
- Doing MLOps with Databricks and MLFlow - Full Course
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Projects
- 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 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
- BentoML | Build Production Grade AI Applications | MLOps
- 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
- 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
- End-to-End MLOps Project using one component on Azure
- MLOps Tutorial - Building a CI/ CD Machine Learning Pipeline
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Youtube channels
- Krish Naik
- DSwithBappy
- MLOps World: Machine Learning in Production
- MLOps Learners
- DataTalksClub
- AiOps & MLOps School
- Miki Bazeley - The MLOps Engineer
- Sokratis Kartakis
- MLOps London
- MLOps Learners
- Krish Naik
- DSwithBappy
- MLOps World: Machine Learning in Production
- DataTalksClub
- AiOps & MLOps School
- Miki Bazeley - The MLOps Engineer
- Sokratis Kartakis
- MLOps London
- MLOps World: Machine Learning in Production
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Introduction
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One Video
- MLOps Course – Build Machine Learning Production Grade Projects
- MLOps Full Course | MLOps Tutorial For Beginners | Machine Learning Operations | Intellipaat
- Introduction to Machine Learning Operations | Ubuntu
- Enterprise MLOps 101 | Nvidia
- Best Practices to Accelerate ML Workflows and Reduce Computational Debt with MLOps | Nvidia
- MLOps Full Course | MLOps Tutorial For Beginners | Machine Learning Operations | Intellipaat
- MLOps Roadmap 2024 | MLOps Career Path 2024 | MLOps Careers | Simplilearn
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Roadmaps
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Communities
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