10 GitHub repositories to master machine learning deployment

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, Introduction

You may have trained countless machine learning models at university or on the job, but have you ever deployed one so that someone could use it via an API or web app? Deployment is where models become products, and it is 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 package models, expose them through APIs, deploy them to the cloud, and build real-world ML-powered applications that you can actually ship and share.

, 1. MLOPS Zoomcamp

store: datatalkclub/mlops-zoomcamp

This repository offers MLOPS ZoomCamp, a free 9-week course on producing ML services.

You’ll learn the basics of MLOps, from training to deployment and monitoring, through 6 structured modules, practical workshops and a final project. Group-based (starting May 5, 2025) or self-paced, with community support via Slack available for learners with Python, Docker, and ML basics.

, 2. Made from ML

store: gokumohandas/made-with-ml

This repository offers a production-grade ML course that teaches you how to build end-to-end ML systems.

You’ll learn the basics of MLOps, 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 System Design

store: chiphuyen/machine-learning-system-design

This repository provides a booklet Project setup, data pipeline, modeling, and serving on machine learning system design are covered.

You’ll learn practical principles through case studies from leading 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

store: alirezadir/production-level-deep-learning

This repository provides a guide for production-level deep learning system design.

You’ll learn four key phases through practical resources and real-world case studies from ML engineers at leading technology companies: project setup, data pipeline, modeling, and service.

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

, 5. Deep Learning in Production Book

store: 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, building 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 wanting production-ready skills.

, 6. Machine Learning + Kafka Streams Example

store: kaivaehner/kafka-stream-machine-learning-example

This repository demonstrates the deployment of analytical models in production using Apache Kafka and its Streams API.

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

, 7. NVIDIA Deep Learning Example for Tensor Core

store: NVIDIA/Deep Learning Example

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 in computer vision, NLP, recommender systems, and speech using frameworks like PyTorch and TensorFlow; Take advantage of automatic mixed precision, multi-GPU/node training, and TensorRT/ONNX transformation for maximum throughput.

, 8. Amazing Production Machine Learning

store: ethicalml/amazing-production-machine-learning

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

You’ll learn to navigate the MLOps ecosystem through classified tool listings, find solutions for deployment, monitoring, and scaling using the built-in search toolkit, and stay up to date with monthly community updates covering everything from AutoML to model serving.

, 9. MLOps Course

store: 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; Implementing end-to-end pipelines with experiment tracking, orchestration, model serving, and monitoring; And create CI/CD workflows for continuous training and deployment.

, 10. MLOP Primer

store: dare-ai/mlops-primer

This repository curates essential MLops resources to help you increase your skills in deploying ML models.

You will learn MLOps tooling scenarios, data-centric AI principles, and production system design through blogs, books, and papers; Explore community resources and courses for practical practice; And lay a foundation for building scalable, responsible machine learning infrastructure.

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

abid ali awan ,@1Abidaliyawan) 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 telecommunications engineering. Their vision is to create AI products using graph neural networks for students struggling with mental illness.