Image by author
# introduction
You may have trained countless machine learning models in college or at work, but have you ever deployed a machine learning model for anyone to use through an API or web app? Deployment is where your model becomes a product, and it's one of the most valuable (and undervalued) skills in modern ML.
In this article, we explore 10 GitHub repositories to master machine learning deployments. These community-driven projects, samples, courses, and a curated list of resources will help you learn how to package your models, expose them via APIs, deploy to the cloud, and build real-world ML-powered applications that you can actually ship and share.
// 1.MLOps Zoom Camp
Repository: DataTalksClub/mlops-zoomcamp
This repository offers MLOps Zoomcamp, a free 9-week course on putting ML services into production.
Learn the fundamentals of MLOps from training to implementation and monitoring through six structured modules, hands-on workshops, and a final project. Available on a cohort basis (starting May 5, 2025) or self-paced, with community support via Slack for learners learning the basics of Python, Docker, and ML.
// 2. Created with ML
Repository: Gokumo Handas/Made-With-ML
This repository provides production-grade ML courses that teach you how to build end-to-end ML systems.
Learn the basics of MLOps, from tracking experiments to serving models. Implement a CI/CD pipeline for continuous deployment. Scale your workloads using Ray/Anyscale. Then, deploy reliable inference APIs and transform your ML experiments into production-ready applications through tested, software-engineered Python scripts.
// 3. Machine learning system design
Repository: Chifuen/Machine learning system design
This repository provides booklets on machine learning system design covering project setup, data pipelines, modeling, and delivery.
Learn practical principles through case studies from leading technology companies, explore 27 open-ended interview questions with answers provided by the community, 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 guidance for production-level deep learning system design.
Learn the four key stages of project setup, data pipelines, modeling, and services through hands-on resources and real-world case studies from ML engineers from leading technology companies.
This guide contains 27 open-ended interview questions with answers provided by the community.
// 5. Books on deep learning in production
Repository: AI Summer/Deep Learning Production Environment
This repository provides Deep Learning In Production, a comprehensive book on building robust ML applications.
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 with TensorFlow Extended and Google Cloud.
This is perfect for software engineers entering DL, researchers with limited software background, and ML engineers looking for production-ready skills.
// 6. Machine Learning + Kafka Streams Example
Repository: kaiwaehner/kafka-streams-machine-learning-examples
This repository shows how to deploy analytical models into production using Apache Kafka and its Streams API.
Learn how to integrate TensorFlow, Keras, H2O, and DeepLearning4J models into scalable streaming pipelines. Implement mission-critical use cases such as flight delay prediction and image recognition with unit tests. It also leverages the Kafka ecosystem for a robust, production-ready ML infrastructure.
// 7. NVIDIA Deep Learning Example for Tensor Cores
Repository: NVIDIA/deep learning example
This repository provides cutting-edge deep learning examples optimized for NVIDIA Tensor Cores on Volta, Turing, and Ampere GPUs.
Learn how to use frameworks like PyTorch and TensorFlow to train and deploy high-performance models across computer vision, NLP, recommender systems, and voice. Maximize throughput with automatic mixed precision, multi-GPU/node training, and TensorRT/ONNX transformations.
// 8. Amazing production machine learning
Repository: EthicalML/Amazing Production Machine Learning
This repository has curated a comprehensive list of open source libraries for production machine learning.
Learn how to navigate the MLOps ecosystem through a categorized list of tools, discover 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 services.
// 9. MLOps Course
Repository: Gokumo Handas/mlops course
This repository offers comprehensive MLOps courses from ML experimentation to production deployment.
Learn how to build production-grade ML applications using software engineering best practices. Scale your workloads using Python, Docker, and cloud platforms. Implement end-to-end pipelines with experiment tracking, orchestration, model serving, and monitoring. Create CI/CD workflows for continuous training and deployment.
// 10. MLOPs Primer
Repository: dair-ai/MLOPs-Primer
This repository has been curated with essential MLOps resources to help you improve your ML model deployment skills.
Learn about the MLOps tool landscape, data-centric AI principles, and production system design through blogs, books, and articles. Find community resources and courses for hands-on practice. and build the foundation for creating a scalable and 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.
| repository | type | main focus |
|---|---|---|
| DataTalksClub/mlops-zoomcamp | structured course | End-to-end MLOps: 9 weeks of roadmap training → implementation → monitoring |
| Gokumo Handas/Made-With-ML | Production ML course | Production-grade ML systems, CI/CD, and scalable services |
| Chifuen/Machine learning system design | Booklet + Q&A | Fundamentals of ML system design, tradeoffs, and interview-style scenarios |
| alirezadir/Production-level deep learning | guide | Production-level DL setup, data pipelines, modeling, and servicing |
| AI-Summer/Deep Learning-Production environment | book | Robust DL applications: tests, pipelines, Docker/Kubernetes, TFX |
| kaiwaehner/kafka-streams-machine-learning-examples | code example | Real-time/streaming ML using Apache Kafka and Kafka Streams |
| NVIDIA/deep learning example | High performance example | GPU-optimized training and inference on NVIDIA Tensor Cores |
| EthicalML/Amazing Production Machine Learning | great list | Hand-picked tools to deploy, monitor, and scale |
| Gokumo Handas/mlops course | MLOps course | Experimentation → Production pipeline, orchestration, service delivery, monitoring |
| dair-ai/MLOPs-Primer | Resource primer | Fundamentals of MLOps, data-centric AI, and production system design |
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 about machine learning and data science technology. Abid holds a master's degree in technology management and a bachelor's degree in communications engineering. His vision is to use graph neural networks to build AI products for students suffering from mental illness.
