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Top 10 Github Repositories To Learn Mlops In 2025

Leo Migdal
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top 10 github repositories to learn mlops in 2025

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. In the age of data-driven decision-making, machine learning (ML) has become a cornerstone for businesses across industries. However, deploying ML models and maintaining them in production requires more than just coding skills; it demands a solid understanding of MLOps (Machine Learning Operations).

To help you navigate this crucial field, we've curated a list of 10 GitHub repositories that offer valuable resources, tools, and frameworks to help you master MLOps. In this article, we will explore, 10 GitHub Repositories to Master MLOps. These 10 GitHub repositories offer a diverse range of tools to help you build, scale, and monitor machine-learning models in production environments. Description: This repository hosts a collection of Jupyter notebooks that showcase the various capabilities of Azure Machine Learning. You'll find practical examples of model training, deployment, and MLOps workflows, making it a great starting point for those interested in Azure's ecosystem. Link: https://github.com/Azure/MachineLearningNotebooks

Description: This repository provides a practical implementation of MLOps using Python and Azure. It covers the entire ML lifecycle—from data preparation to deployment and monitoring—making it an excellent resource for hands-on learning. CMU course that covers how to build, deploy, assure, and maintain software products with machine-learned models. Includes the entire lifecycle from a prototype ML model to an entire system deployed in production. Covers also responsible AI (including safety, security, fairness, explainability) and MLOps. For earlier offerings see websites for Fall 2019, Summer 2020, Fall 2020, Spring 2021 Spring 2022, Fall 2022, Spring 2023, Spring 2024, and Fall 2024.

This Spring 2025 offering is designed for students with some data science experience (e.g., has taken a machine learning course, has used sklearn) and basic programming skills (e.g., basic Python programming with libraries, can... Going forward we expect to offer this course at least every spring semester and possibly some fall semesters (not summer semesters). Waitlist: we often cannot accommodate all interested students in Spring semesters, though we expect there to be waitlist movement. We encourage students who are able to move to alternative labs with space, to do so. For students enrolled in 17-XXX numbers, contact Jenni Cooper (cooperj@andrew.cmu.edu) for assistance; for students enrolled in 11-XXX numbers, contact Amber Vivis (albrown@andrew.cmu.edu) and Karen Kirk (karensuk@andrew.cmu.edu). Note that the instructors cannot help with waitlist/registration movement, please contact the course admins instead!

For researchers, educators, or others interested in this topic, we share all course material, including slides and assignments, under a creative commons license on GitHub (https://github.com/mlip-cmu) and have also published a textbook with chapters... A while ago we also wrote an article describing the rationale and the initial design of this course: Teaching Software Engineering for AI-Enabled Systems. Video recordings of the Summer 2020 offering are online on the course page, though they are a bit outdated by now. We would be happy to see this course or a similar version taught at other universities. See also an annotated bibliography on research in this field. This is a course for those who want to build software products with machine learning, not just models and demos.

We assume that you can train a model or build prompts to make predictions, but what does it take to turn the model into a product and actually deploy it, have confidence in its... The course is designed to establish a working relationship between software engineers and data scientists: both contribute to building ML-enabled systems but have different expertise and focuses. To work together they need a mutual understanding of their roles, tasks, concerns, and goals and build a working relationship. This course is aimed at software engineers who want to build robust and responsible products meeting the specific challenges of working with ML components and at data scientists who want to understand the requirements... The course is a good fit for student looking at a career as an ML engineer. The course focuses on all the steps needed to turn a model into a production system in a responsible and reliable manner.

Machine learning (ML) is one of the fastest-growing fields in technology, and learning it effectively requires access to high-quality resources. GitHub is a treasure trove of ML projects, tutorials, and tools that can help both beginners and advanced practitioners sharpen their skills. In this article, we explore some of the best GitHub repositories for learning and applying ML concepts, categorized by skill level and focus area. For those new to ML, structured courses and hands-on tutorials can make the learning curve smoother. Here are some excellent GitHub repositories to start with: Once you have a basic understanding of ML, hands-on projects help reinforce concepts.

The following repositories provide excellent project-based learning opportunities: For experienced ML engineers, leveraging state-of-the-art tools can lead to cutting-edge applications. These repositories offer advanced techniques and frameworks: For those interested in specialized ML domains like reinforcement learning or NLP, the following repositories offer deep insights and advanced projects: Begin your MLOps journey with these comprehensive free resources available on GitHub. It is becoming more important to master MLOps (Machine Learning Operations) 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). Luckily, the open-source community has created numerous resources to assist beginners in mastering these concepts and tools. Here are ten GitHub repositories that are essential for anyone looking to master MLOps: 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 build an end-to-end MLOps project, and each week will focus on a specific topic to help you achieve this goal. The repository provides MLOps end-to-end examples & solutions.

A collection of examples showing different end to end scenarios operationalizing ML workflows with Azure Machine Learning, integrated with GitHub and other Azure services such as Data Factory and DevOps. It is becoming more important to master MLOps (Machine Learning Operations) 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). Luckily, the open-source community has created numerous resources to assist beginners in mastering these concepts and tools. Here are ten GitHub repositories that are essential for anyone looking to master MLOps: 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 build an end-to-end MLOps project, and each week will focus on a specific topic to help you achieve this goal. The repository provides MLOps end-to-end examples & solutions. A collection of examples showing different end to end scenarios operationalizing ML workflows with Azure Machine Learning, integrated with GitHub and other Azure services such as Data Factory and DevOps. If you are looking for MLOps end-to-end examples and solutions, this repository has got you covered. It contains a diverse collection of scenarios that demonstrate how to operationalize ML workflows using Azure Machine Learning. Plus, it is integrated with other Azure services like Data Factory and DevOps, as well as GitHub.

Model development requires structured deployment and monitoring to remain reliable over time. Consistent data and environment control prevent accuracy loss and unexpected failures. Automation supports continuous training, scaling, and performance tracking across usage stages. Machine learning models are becoming part of daily life. Music apps recommend new songs, maps show the best route, and online stores suggest products. These systems work because models are trained and then managed carefully after deployment.

MLOps is the process that keeps these models running smoothly. It connects model development with how it is used in real conditions. For students who are exploring machine learning in 2025, some projects give a clear idea of how a model moves from a classroom experiment to a working application. Each project below teaches one important part of model handling in simple and practical ways. Imagine having a treasure chest of tools, tutorials, and cutting-edge projects at your fingertips—all curated by the global developer community. That’s GitHub in 2025.

With millions of repositories, it’s easy to feel overwhelmed. But fear not! We’ve handpicked the top 10 GitHub repositories that will supercharge your coding journey, whether you’re building AI apps, diving into quantum computing, or just starting out. Let’s explore these gems, complete with real-world examples and tips to make the most of them. Link: github.com/tensorflow/tensorflowWhat it offers: Google’s open-source machine learning framework for building and deploying models.Why follow in 2025: AI isn’t slowing down. TensorFlow remains essential for everything from chatbots to self-driving cars.

Link: github.com/microsoft/TypeScriptWhat it offers: A typed superset of JavaScript for building robust, error-free apps.Why follow in 2025: TypeScript’s adoption is soaring, especially in enterprise environments. Link: github.com/vercel/next.jsWhat it offers: A React framework for server-side rendering, static sites, and full-stack apps.Why follow in 2025: Next.js dominates modern web development with its SEO-friendly, high-performance apps. Link: github.com/ossu/computer-scienceWhat it offers: A complete, open-source curriculum for self-taught developers.Why follow in 2025: Foundational CS knowledge never goes out of style.

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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...

GitHub Is Still One Of The Best Ways To Gain

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. In the age of data-driven decision-making, machine learning (ML) has become...

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This Spring 2025 Offering Is Designed For Students With Some

This Spring 2025 offering is designed for students with some data science experience (e.g., has taken a machine learning course, has used sklearn) and basic programming skills (e.g., basic Python programming with libraries, can... Going forward we expect to offer this course at least every spring semester and possibly some fall semesters (not summer semesters). Waitlist: we often cannot accommodat...