10 GitHub Repositories for Master Machine Learning Deployments

Photo by author

# Introduction

Maybe you’ve trained countless machine learning models at university or on the job, but have you ever deployed one so that anyone could use it through an API or 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’ll explore 10 GitHub repositories for master machine learning deployments. 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. Match Zoomcamp

Storage: Data Talks Club/MLOPS Zoomcamp

This repository provides MLOps ZoomCamp, a free 9-week course to prepare for ML services.

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

// 2. Made with ML

Storage: Gokumohindas/Made-Ml

This repository provides a production-grade ML course that teaches you how to build an end-to-end ML system.

You’ll learn the fundamentals of MLOPs, from tracking experience to serving models. Implement CI/CD pipelines for continuous deployment. Workload scales with ray/any scale. And deploy reliable inference APIs—turning ML experiences into production-ready applications through tested, software-engineered Python scripts.

// 3. Machine Learning System Design

Storage: Chiphuyen/Machine Learning System Design

This collection a Booklet Covers project setup, data pipelines, modeling, and service on machine learning system design.

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

// 4. A guide to production-level deep learning

Storage: Alerzadeer/Production Level Deep Learning

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

You’ll learn four key steps: project setup, data pipelines, modeling, and service, through practical resources and real-world case studies from ML engineers at major tech companies.

The guide includes 27 interview questions with community-driven answers.

// 5. Deep learning in the production book

Storage: AI-Summer/Deep Learning in Production

This repository provides deep learning in production, a comprehensive book for building robust ML applications.

You’ll learn best practices for writing and testing DL code, building efficient data pipelines, rendering models with Flask/UWSGI/NGINX, deploying with Docker/Kabernets, and implementing end-to-end MLops using TensorFlow extensions 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 is an example of examples

Storage: Kauehner/Kafka Streams Machine Learning-Examples

This repository demonstrates deploying analytical 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. Perform mission-critical use cases such as flight delay prediction and image recognition with unit tests. and Kafka’s ecosystem for a robust, production-ready ML infrastructure.

// 7. Nvidia Deep Learning Examples for Tensor Core

Storage: nvidia/deeplearningexamples

This repository optimizes the latest deep learning instances for NVIDIA tensor cores on Volta, Turing, and Ampere GPUs.

You will learn to train and deploy high-performance models of speech using computer vision, NLP, recommender systems, and frameworks such as PyTorch and TensorFlow. Automatic mixed precision, multi-GPU/node training, and Tensort/ONX conversion for maximum throughput.

// 8. Great Production Machine Learning

Storage: Ethics/Awesome Production Machine Learning

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

You’ll navigate the MLOPs ecosystem through categorized tool lists, discover deployment, monitoring, and scaling solutions using the built-in search toolkit, and stay current with monthly community updates covering everything from automation to service models.

// 9. MLOPS Course

Storage: Gokumohandas/mlops course

This repository provides a comprehensive MLOPS course that takes you from ML experiments to production deployment.

You’ll 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 experience tracking, orchestration, service models and monitoring. And create CI/CD workflows for continuous training and deployment.

// 10. MLOPS Primer

Storage: Dire-A/MLOPS primer

This repository provides the necessary MLOPS resources to help you deploy ML models.

You’ll learn MLOPs tooling landscape, data-centric AI principles, and production system design through blogs, books, and papers. Explore community resources and courses for practice. And create a foundation for building a scalable, responsive machine learning infrastructure.

Storage map

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

Abid Ali Owan For centuries.@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 create an AI product using graph neural networks for students with mental illness.