You may have trained many machine learning models during college or at work. But have you ever put one online so others can actually use it through a website or an API? That step—deployment—is where machine learning turns into a real product. It’s also one of the most useful and often ignored skills in the ML world.
Training a model is only half the job. Deployment teaches you how to package your model, serve it through an API, run it in the cloud, and keep it working in the real world. If you want your ML work to matter beyond notebooks, this skill is essential.
In this article, you’ll find 10 GitHub repositories that can help you learn machine learning deployment properly. These projects, guides, and courses are built by the community and focus on real-world skills. They will help you move from experiments to working ML applications you can share and ship.
1. MLOps Zoomcamp
Repository: DataTalksClub/mlops-zoomcamp
MLOps Zoomcamp is a free, structured course that teaches you how to take ML models into production. The course runs for nine weeks and covers the full journey—from training to deployment and monitoring.
You’ll work through clear modules, hands-on workshops, and a final project. You can join a live cohort or learn at your own pace. A Slack community is available for help and discussion, making it easier to stay on track.
This is a great choice if you already know basic Python, Docker, and machine learning and want to level up.
2. Made With ML
Repository: GokuMohandas/Made-With-ML
This repository shows how real production ML systems are built from start to finish. Instead of toy examples, you’ll work with clean, well-tested code that mirrors real applications.
You’ll learn how to track experiments, build deployment pipelines, serve models through APIs, and automate updates using CI/CD. It focuses strongly on software engineering best practices, which many ML engineers lack early on.
Perfect for turning messy experiments into reliable products.
3. Machine Learning Systems Design
Repository: chiphuyen/machine-learning-systems-design
This project is more about thinking than coding. It explains how ML systems are designed in real companies, from data collection to serving predictions.
You’ll find practical lessons, case studies from large tech firms, and open-ended interview questions with answers contributed by the community. It’s especially useful if you’re preparing for ML system design interviews or want to understand how big systems work.
4. A Guide to Production-Level Deep Learning
Repository: alirezadir/Production-Level-Deep-Learning
This guide walks through what it really takes to run deep learning systems in production. It focuses on four main areas: project setup, data pipelines, model training, and serving.
The content is practical and backed by real examples from ML engineers working at large companies. Like the previous resource, it also includes interview-style questions that help you think more clearly about system design.
5. Deep Learning in Production (Book)
Repository: The-AI-Summer/Deep-Learning-In-Production
This repository hosts a full book focused on building strong and reliable deep learning applications. It covers everything from writing clean code to testing, deployment, and monitoring.
You’ll learn how to serve models using tools like Flask and Docker, manage infrastructure with Kubernetes, and build full MLOps pipelines using cloud services.
It’s a solid resource for software engineers moving into ML, researchers who want real-world skills, and ML engineers aiming to build production-ready systems.
6. Machine Learning with Kafka Streams
Repository: kaiwaehner/kafka-streams-machine-learning-examples
This repository shows how machine learning can work with real-time data. It uses Apache Kafka to process streams of data and apply ML models at scale.
You’ll see examples like predicting flight delays and running image recognition in live systems. The focus is on reliability, testing, and real-world performance.
Best suited for engineers interested in streaming data and large-scale systems.
7. NVIDIA Deep Learning Examples
Repository: NVIDIA/DeepLearningExamples
This collection shows how to train and deploy high-performance deep learning models using NVIDIA GPUs. The examples are optimized for speed and efficiency.
You’ll find projects covering vision, language, speech, and recommendation systems. The repo also teaches how to use techniques like mixed precision and multi-GPU training.
Ideal if performance and scale matter in your work.
8. Awesome Production Machine Learning
Repository: EthicalML/awesome-production-machine-learning
This is a curated list of tools and libraries used in production ML. Instead of teaching one method, it helps you explore the full MLOps ecosystem.
You’ll discover tools for deployment, monitoring, data management, and scaling. It’s updated regularly and is great for staying informed about what’s popular and reliable in the industry.
9. MLOps Course
Repository: GokuMohandas/mlops-course
This course takes you step by step from ML experiments to production systems. It focuses on clean design, automation, and scaling.
You’ll learn how to build full pipelines, track models, serve predictions, and monitor performance. It also covers CI/CD so your models can be updated safely and automatically.
A strong option if you want a complete and practical learning path.
10. MLOps Primer
Repository: dair-ai/MLOps-Primer
This repository is a beginner-friendly entry point into MLOps. It collects blogs, books, papers, and courses that explain how ML deployment works.
You’ll learn about tools, system design, data-focused ML, and responsible AI practices. It’s a great place to start if deployment feels overwhelming.
Final Thoughts
Machine learning deployment is what separates a project from a product. These GitHub repositories cover different parts of that journey—from basics to advanced systems.
You don’t need to study all of them. Pick one that matches your level and goals. Once you learn how to deploy models properly, your ML work becomes far more valuable—and far more impactful.
