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Mastering MLOps (Machine Learning Operations) is becoming increasingly important for those who want to effectively deploy, monitor, and maintain their ML models in production. MLOps is a set of practices that aims to merge ML system development (Dev) and ML system operation (Ops). Fortunately, the open source community has created numerous resources to help beginners master these concepts and tools.
Here are ten GitHub repositories that are essential for anyone looking to master MLOps:
GitHub link: engraving/MLOps-Basics
It is a 9-week study plan designed to help you master various concepts and tools related to model monitoring, configurations, data versioning, model packaging, Docker, GitHub Actions, and AWS Cloud. You will learn how to create an end-to-end MLOps project and each week will focus on a specific topic to help you achieve this goal.
GitHub link: microsoft/MLOps
The repository provides end-to-end MLOps examples and solutions. A collection of examples showing different end-to-end scenarios that operationalize machine learning workflows with Azure Machine Learning, integrated with GitHub and other Azure services such as Data Factory and DevOps.
GitHub link: GokuMohandas/Made-With-ML
If you're looking for comprehensive MLOps examples and solutions, this repository has you covered. Contains a diverse collection of scenarios that demonstrate how to operationalize machine learning workflows using Azure Machine Learning. Additionally, it is integrated with other Azure services such as Data Factory and DevOps, as well as GitHub.
GitHub link: Pythondeveloper6/Awesome-MLOPS
The repository contains links to various free resources available online for MLOps. These resources include YouTube videos, career roadmaps, LinkedIn accounts to follow, books, blogs, free and paid courses, communities, projects, and tools. You can find almost everything related to MLOps in one place, so instead of searching for various things online, you can simply visit the repository and learn.
GitHub link: mlops-guide/mlops-guide.github.io
The repository will take you to a static site hosted on GitHub that will help projects and companies build a more reliable MLOps environment. Covers MLOP principles, implementation guides, and project workflow.
GitHub link: kelvins/incredible-mlops
The repository contains a list of MLOps tools that can be used for AutoML, CI/CD for machine learning, cron job monitoring, data catalog, data enrichment, data exploration, data management, data processing, data validation , data visualization, drift detection. Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platform, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing and Validation, Optimization Tools , simplification and analysis and visual debugging tools.
GitHub link: ShaftNicki/dtu_mlops
This is a repository for the DTU. course 02476, which includes additional exercises and materials for the Machine Learning Trading course. The course lasts three weeks and covers topics such as development practices, reproducibility, automation, cloud services, deployment, and advanced topics such as monitoring and scaling for machine learning applications.
GitHub link: GokuMohandas/mlops-course
The course focuses on teaching students how to design, develop, deploy, and iterate production-grade machine learning applications using best practices, scaling machine learning workloads, integrating MLOps components, and creating CI/CD workflows. for continuous improvement and seamless implementation.
GitHub link: DataTalksClub/mlops-zoomcamp
One of my favorite courses to learn a new concept by building a project. DataTalks.Club's MLOps course teaches the practical aspects of putting machine learning services into production, from training and experimentation to model deployment and monitoring. It is designed for data scientists, machine learning engineers, software engineers, and data engineers who are interested in learning how to put machine learning workflows into practice.
GitHub link: featurestoreorg/serverless-ml-course
This course focuses on developing complete machine learning systems with serverless capabilities. It allows developers to create predictive services without requiring experience in Kubernetes or cloud computing. They can do this by writing Python programs and using serverless functions, inference pipelines, function stores, and model registries.
Mastering MLOps is essential to ensure the reliability, scalability, and efficiency of machine learning projects in production. The repositories listed above offer a wealth of knowledge, practical examples, and essential tools to help you understand and apply MLOps principles effectively. Whether you're a beginner looking to get started or an experienced practitioner looking to deepen your knowledge, these resources provide valuable information and guidance on your path to mastering MLOps.
Check out the ai learning platform called travis, which can help you master MLOps and its concepts faster. Travis generates explanations about the topic and you can ask follow-up questions. Additionally, you can do your own research as it provides links to blogs and tutorials published by major publications on Medium, Substacks, independent blogs, official documentation, and books.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a master's degree in technology management and a bachelor's degree in telecommunications engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
