10 GitHub Repositories to Master Machine Learning Deployment

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# Introduction

 
You might have trained countless machine learning models at university or on the job, but have you ever deployed one so that anyone can use it through an API or a web app? Deployment is where models become products, and it’s one of the most valuable (and underrated) skills in modern ML.

In this article, we will explore 10 GitHub repositories to master machine learning deployment. These community-driven projects, examples, courses, and curated resource lists will help you learn how to package models, expose them via APIs, deploy them to the cloud, and build real-world ML-powered applications you can actually ship and share.

// 1. MLOps Zoomcamp

Repository: DataTalksClub/mlops-zoomcamp

This repository provides MLOps Zoomcamp, a free 9-week course on productionizing ML services. 

You will learn MLOps fundamentals from training to deployment and monitoring through 6 structured modules, hands-on workshops, and a final project. Available cohort-based (starting May 5, 2025) or self-paced, with community support via Slack for learners with Python, Docker, and ML basics.

// 2. Made With ML

Repository: GokuMohandas/Made-With-ML

This repository delivers a production-grade ML course teaching you to build end-to-end ML systems. 

You will learn MLOps fundamentals from experiment tracking to model serving; implement CI/CD pipelines for continuous deployment; scale workloads with Ray/Anyscale; and deploy reliable inference APIs—transforming ML experiments into production-ready applications through tested, software-engineered Python scripts.

// 3. Machine Learning Systems Design

Repository: chiphuyen/machine-learning-systems-design

This repository provides a booklet on machine learning systems design covering project setup, data pipelines, modeling, and serving. 

You will learn practical principles through case studies from major tech companies, explore 27 open-ended interview questions with community-contributed answers, and discover resources for building production ML systems.

// 4. A Guide to Production Level Deep Learning

Repository: alirezadir/Production-Level-Deep-Learning

This repository provides a guide to production-level deep learning systems design. 

You will learn the four key stages: project setup, data pipelines, modeling, and serving, through practical resources and real-world case studies from ML engineers at major tech companies. 

The guide includes 27 open-ended interview questions with community-contributed answers.

// 5. Deep Learning In Production Book

Repository: The-AI-Summer/Deep-Learning-In-Production

This repository provides Deep Learning In Production, a comprehensive book on building robust ML applications. 

You will learn best practices for writing and testing DL code, constructing efficient data pipelines, serving models with Flask/uWSGI/Nginx, deploying with Docker/Kubernetes, and implementing end-to-end MLOps using TensorFlow Extended and Google Cloud.

It is ideal for software engineers entering DL, researchers with limited software background, and ML engineers seeking production-ready skills.

// 6. Machine Learning + Kafka Streams Examples

Repository: kaiwaehner/kafka-streams-machine-learning-examples

This repository demonstrates deploying analytic models to production using Apache Kafka and its Streams API. 

You will learn to integrate TensorFlow, Keras, H2O, and DeepLearning4J models into scalable streaming pipelines; implement mission-critical use cases like flight delay prediction and image recognition with unit tests; and leverage Kafka’s ecosystem for robust, production-ready ML infrastructure.

// 7. NVIDIA Deep Learning Examples for Tensor Cores

Repository: NVIDIA/DeepLearningExamples

This repository provides state-of-the-art deep learning examples optimized for NVIDIA Tensor Cores on Volta, Turing, and Ampere GPUs. 

You will learn to train and deploy high-performance models across computer vision, NLP, recommender systems, and speech using frameworks like PyTorch and TensorFlow; leverage automatic mixed precision, multi-GPU/node training, and TensorRT/ONNX conversion for maximum throughput.

// 8. Awesome Production Machine Learning

Repository: EthicalML/awesome-production-machine-learning

This repository curates a comprehensive list of open source libraries for production machine learning. 

You will learn to navigate the MLOps ecosystem through categorized tool listings, discover solutions for deployment, monitoring, and scaling using the built-in search toolkit, and stay current with monthly community updates covering everything from AutoML to model serving.

// 9. MLOps Course

Repository: GokuMohandas/mlops-course

This repository provides a comprehensive MLOps course taking you from ML experimentation to production deployment. 

You will learn to build production-grade ML applications following software engineering best practices; scale workloads using Python, Docker, and cloud platforms; implement end-to-end pipelines with experiment tracking, orchestration, model serving, and monitoring; and create CI/CD workflows for continuous training and deployment.

// 10. MLOPs Primer

Repository: dair-ai/MLOPs-Primer

This repository curates essential MLOps resources to help you upskill in deploying ML models. 

You will learn the MLOps tooling landscape, data-centric AI principles, and production system design through blogs, books, and papers; discover community resources and courses for hands-on practice; and build a foundation for creating scalable, responsible machine learning infrastructure.

Repository Map

 
Here’s a quick comparison table to help you understand how each repository fits into the broader ML deployment ecosystem:

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.