10 GitHub Repositories to Master Machine Learning Deployment

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# Entry

You may have trained countless machine learning models at university or at work, but have you ever deployed one so that anyone can apply it via an API or web application? Deployment is when models become products, and it is one of the most valuable (and undervalued) skills in state-of-the-art machine learning.

In this article, we will discuss 10 GitHub repositories where you can master your machine learning implementation. These projects, examples, courses, and curated lists of community-led resources will assist you learn to package models, expose them via APIs, deploy them to the cloud, and create real ML-powered applications that you can actually ship and share.

// 1. MLOps Zoomcamp

Warehouse: DataTalksClub/mlops-zoomcamp

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

You will learn the basics of MLOps, from training to implementation and monitoring, through 6 structured modules, practical workshops and a final project. Available on a cohort basis (starting May 5, 2025) or self-paced, with community support via Slack for students familiar with Python, Docker and ML fundamentals.

// 2. Made of ML

Warehouse: GokuMohandas/Made-With-ML

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

You’ll learn the basics of MLOps, from tracking experiments to sharing models; implement CI/CD pipelines for continuous deployment; scale workloads with Ray/Anyscale; and deploy resilient inference APIs, transforming ML experiments into production-ready applications with tested, software-developed Python scripts.

// 3. Designing machine learning systems

Warehouse: chiphuyen/machine learning system design

This repository provides, among others: booklet on the design of machine learning systems including project configuration, data pipelines, modeling and sharing.

You’ll learn practical principles through case studies of top technology companies, explore 27 open-ended interview questions with community-submitted answers, and discover resources for building production machine learning systems.

// 4. A guide to deep learning for production

Warehouse: alirezadir/Production-level deep learning

This repository provides a guide to designing production-grade deep learning systems.

You’ll learn the four key stages of project setup, data pipelines, modeling, and sharing with hands-on resources and real-world case studies from ML engineers at top technology companies.

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

// 5. Deep Learning in Manufacturing book.

Warehouse: Lato-AI/Deep learning in production

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

You’ll learn best practices for writing and testing DL code, constructing productive data pipelines, sharing 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 starting at the DL level, researchers with confined software experience, and ML engineers looking for production-ready skills.

// 6. Machine learning + Kafka stream examples

Warehouse: kaiwaehner/kafka-streams-machine learning examples

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

You’ll learn how to integrate TensorFlow, Keras, H2O, and DeepLearning4J models with scalable streaming pipelines; implement mission-critical apply cases such as flight delay prediction and image recognition with unit testing; and leverage the Kafka ecosystem to create a resilient, production-ready ML infrastructure.

// 7. NVIDIA Deep Learning Examples for Tensor Cores

Warehouse: NVIDIA/DeepLearning examples

This repository contains 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 platforms such as PyTorch and TensorFlow; Leverage automatic mixed precision, multi-GPU/node training, and TensorRT/ONNX conversion for maximum throughput.

// 8. Incredible learning of production machines

Warehouse: Ethical ML/Amazing Machine Learning in Manufacturing

This repository contains an extensive list of open source libraries for production machine learning.

You’ll learn to navigate the MLOps ecosystem through categorized tool lists, discover solutions for deploying, monitoring, and scaling with the built-in search toolkit, and stay up to date with monthly community updates covering everything from AutoML to model sharing.

// 9. MLOps course

Warehouse: GokuMohandas/mlops rate

This repository provides a comprehensive MLOps course from ML experiments to production deployment.

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

// 10. MLOP basics

Warehouse: foundation-dair-ai/MLOPs

This repository contains crucial MLOps resources to assist you improve your skills in implementing machine learning models.

From blogs, books and articles, you will learn about the landscape of MLOps tools, the principles of data-centric artificial intelligence and the design of production systems; discover community resources and hands-on courses; and build the foundation for a scalable, accountable machine learning infrastructure.

Repository map

Here’s a quick comparison chart to assist you understand how each repository fits into the broader machine learning deployment ecosystem:

Abid Ali Awan (@1abidaliawan) is a certified data science professional who loves building machine learning models. Currently, he focuses on creating content and writing technical blogs about machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunications Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.