Top 10 GitHub Repositories to Learn Machine Learning Deployment in 2025
Top 10 GitHub Repositories to Learn Machine Learning Deployment in 2025
Top 10 GitHub : Most people who study machine learning spend their time training models—but far fewer know how to ship those models into real applications. Deployment is where ML becomes real: APIs, cloud services, automation, CI/CD, monitoring, scaling, and everything in between.
To help you build that skill set, here are 10 outstanding GitHub repositories that teach practical ML deployment through courses, examples, guides, and curated resources.
Top 10 GitHubÂ
1. MLOps Zoomcamp
Repo: DataTalksClub/mlops-zoomcamp
A free, intensive 9-week program that walks you through the entire lifecycle of production machine learning.
You’ll explore:
- MLOps foundations
- Packaging and deploying models
- Experiment tracking
- Workflow orchestration
- Production monitoring
Available as a self-paced program or a guided cohort (next one starts May 5, 2025). Community support is provided via Slack.
2. Made With ML
Repo: GokuMohandas/Made-With-ML
A production-first ML engineering course designed to help you build reliable ML systems.
You’ll learn how to:
- Implement CI/CD for ML projects
- Track experiments and version models
- Deploy scalable inference services
- Use Ray/Anyscale for distributed workloads
It’s a hands-on, software-engineering-focused approach to ML.
3. Machine Learning Systems Design
Repo: chiphuyen/machine-learning-systems-design
This repo hosts a practical booklet on designing ML systems.
You’ll dive into:
- Data pipelines
- Model lifecycle decisions
- Serving and scalability concepts
- Trade-offs used in real-world systems
It also includes 27 open-ended design questions with community explanations—great for interviews.
4. Production-Level Deep Learning
Repo: alirezadir/Production-Level-Deep-Learning
A streamlined guide that breaks production deep learning into four essential components:
- Initial project setup
- Data pipelines
- Model development
- Model serving
Includes examples, explanations, and interview-style questions to help you think like an ML systems engineer.
5. Deep Learning in Production (Book)
Repo: The-AI-Summer/Deep-Learning-In-Production
A deep dive into how to build robust, maintainable deep-learning systems.
Topics include:
- Writing testable DL code
- Efficient data pipelines
- Serving with Flask/uWSGI/Nginx
- Docker + Kubernetes deployments
- End-to-end pipelines with TFX and Google Cloud
Ideal for researchers, ML engineers, and software developers moving toward production.
6. ML + Kafka Streams Examples
Repo: kaiwaehner/kafka-streams-machine-learning-examples
A collection of real-time ML deployment examples using Apache Kafka.
You’ll learn to:
- Embed ML models (TensorFlow, Keras, H2O, DL4J) into streaming apps
- Build real-time pipelines
- Implement use cases like flight delay prediction and image processing
- Add tests and monitoring for mission-critical streaming ML systems
Great for anyone interested in online or continuous inference.
7. NVIDIA Deep Learning Examples
Repo: NVIDIA/DeepLearningExamples
A suite of optimized ML training and inference pipelines for NVIDIA GPUs.
You’ll practice:
- Using Tensor Cores for maximum throughput
- Training CV, NLP, recommender, and speech models
- Running mixed-precision and multi-GPU training
- Converting models to ONNX/TensorRT for deployment
If you want high-performance ML, this is the gold standard.
8. Awesome Production Machine Learning
Repo: EthicalML/awesome-production-machine-learning
A massive, community-maintained directory of tools for production ML, including:
- Deployment frameworks
- Model monitoring systems
- Feature stores
- Orchestration tools
- Testing and CI/CD systems
Updated monthly and extremely helpful when choosing tools for new projects.
9. MLOps Course
Repo: GokuMohandas/mlops-course
Another excellent course that takes you from experimentation to productionized ML systems.
You’ll learn how to:
- Build complete ML pipelines
- Track and version experiments
- Orchestrate workflows
- Deploy and monitor models
- Develop CI/CD pipelines
A practical companion to real-world ML engineering.
10. MLOps Primer
Repo: dair-ai/MLOPs-Primer
A compact but powerful introduction to the ML operations landscape.
It includes curated links to:
- Articles
- Books
- Community projects
- Tools and frameworks
Top 10 GitHub Repository Map
Covering topics such as data-centric AI, infrastructure design, and deployment best practices.
| Repository | Type | Main Focus |
|---|---|---|
| mlops-zoomcamp | Structured course | Full MLOps lifecycle: training → deployment → monitoring |
| Made-With-ML | Practical course | Production ML systems, CI/CD, scalable serving |
| machine-learning-systems-design | Booklet + Q&A | Systems design principles, trade-offs, interview prep |
| Production-Level-Deep-Learning | Guide | Deep learning deployment fundamentals |
| Deep-Learning-In-Production | Book | Robust DL engineering, Docker/Kubernetes, TFX |
| kafka-streams-ml-examples | Code examples | Streaming ML with Apache Kafka |
| DeepLearningExamples | High-performance code | NVIDIA GPU-optimized ML pipelines |
| awesome-production-machine-learning | Awesome list | Tools for deployment, monitoring, scaling |
| mlops-course | MLOps course | End-to-end ML pipelines, orchestration, serving |
| MLOPs-Primer | Resource guide | Fundamentals, tooling, data-centric practices |
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