GitHub repositories provide hands-on learning of real-world MLOps workflows.
Tools like MLflow, Kubeflow, and DVC show how scaling and tracking work in practice.
Beginner-friendly repos make it easier to move from AI experiments to deployment.
Machine Learning Operations (MLOps) has developed into an important space in the world of AI. Building a model within a notebook is just the first step; the trick is making sure that the model works in the real world. MLOps is essential for helping shift machine learning projects from a proof-of-concept pace to production.
GitHub is still one of the best ways to gain these understanding of MLOps. There are many open-source repositories. Github is a great place to find where developers and organizations will share code, tools, and practical examples. Here are ten GitHub repositories that learners can benefit from concerning MLOps in practice.
This repository is a large collection of Jupyter notebooks created by Microsoft. It covers every stage of a machine learning workflow, including training, testing, and deployment. Learners can follow these notebooks to see how real projects are built in Azure’s cloud environment.
Microsoft also maintains this Python-based repository focused on end-to-end pipelines. It demonstrates how to handle data, train models, and track experiments. The examples make it easier to see how MLOps connect different steps in a project.
Also Read: Top MLOps Tools for Scaling Machine Learning Operations
This repository is designed for step-by-step learning and instruction. It introduces frameworks, coding examples, and structured workflows that guide beginners through the basics of putting models into production. Its clear layout makes it a popular choice for first-time learners.
Seldon Core is an advanced tool for deploying machine learning models on Kubernetes. The repository includes examples of scalable serving, testing, and monitoring. Teams that want to deliver models at scale often use Seldon as a reliable platform.
MLflow is one of the most widely used tools in MLOps. Its GitHub repository provides experiment tracking, model packaging, and deployment methods. Many organizations use MLflow because it is compatible with multiple cloud platforms and integrates easily with existing projects.
Created by Netflix, Metaflow helps manage machine learning workflows. The repository focuses on reproducibility, ensuring that experiments can be repeated and results remain consistent. It is popular among developers working on large projects with many moving parts.
Kubeflow is an open-source project supported by Google. It is designed to bring machine learning to Kubernetes. The repository provides tools for running training jobs, managing notebooks, and building pipelines. Its modular design allows teams to pick the parts they need.
Data Version Control, or DVC, brings Git-style versioning to datasets and models. This repository is handy for tracking data changes and making projects reproducible. As machine learning projects continue to grow in size, DVC has become increasingly essential for organization and collaboration.
This repository contains build scripts and automation examples for running MLOps on Google Cloud. It helps developers understand how to connect containerized applications with cloud workflows. Those working in Google’s ecosystem often start here.
This repository is a complete course that teaches MLOps from start to finish. It covers everything from experiments to continuous deployment. The focus is on building production-ready projects with best practices, making it a strong option for learners who want structured guidance.
Each repository highlighted demonstrates the authentic challenges of today's machine learning ecosystem. To illustrate, we can first look at MLflow and DVC, which both track experiments and data.
Seldon Core and Kubeflow optimally demonstrate scaling & automation in modern production environments. Another important theme in machine learning preparation is beginner-friendly repositories, such as DataSciBoy/MLOps and mlops-course, both noted for their structured approach to allowing anyone to start.
Organizations across every sector have rapidly adopted machine learning at scale. From streaming services to health systems, being able to manage, deploy and monitor models has become as essential as building models. MLOps has gone from being optional to being a mandate for success in the real-world.
GitHub has been a significant factor in this growth. With thousands of developers contributing, learners can learn independently in theory and access real-world examples. By examining these ten repositories, developers now possess the knowledge and practical application to understand how MLOps is powering today's AI applications.