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# Introduction
You may need skilled numerous machine studying fashions at college or on the job, however have you ever ever deployed one in order that anybody can use it by means of an API or an internet app? Deployment is the place fashions develop into merchandise, and it’s one of the crucial invaluable (and underrated) expertise in fashionable ML.
On this article, we’ll discover 10 GitHub repositories to grasp machine studying deployment. These community-driven initiatives, examples, programs, and curated useful resource lists will make it easier to learn to bundle fashions, expose them by way of APIs, deploy them to the cloud, and construct real-world ML-powered functions you possibly can really ship and share.
// 1. MLOps Zoomcamp
Repository: DataTalksClub/mlops-zoomcamp
This repository supplies MLOps Zoomcamp, a free 9-week course on productionizing ML companies.
You’ll study MLOps fundamentals from coaching to deployment and monitoring by means of 6 structured modules, hands-on workshops, and a remaining mission. Out there cohort-based (beginning Could 5, 2025) or self-paced, with neighborhood assist by way of Slack for learners with Python, Docker, and ML fundamentals.
// 2. Made With ML
Repository: GokuMohandas/Made-With-ML
This repository delivers a production-grade ML course educating you to construct end-to-end ML methods.
You’ll study MLOps fundamentals from experiment monitoring to mannequin serving; implement CI/CD pipelines for steady deployment; scale workloads with Ray/Anyscale; and deploy dependable inference APIs—reworking ML experiments into production-ready functions by means of examined, software-engineered Python scripts.
// 3. Machine Studying Programs Design
Repository: chiphuyen/machine-learning-systems-design
This repository supplies a booklet on machine studying methods design protecting mission setup, information pipelines, modeling, and serving.
You’ll study sensible rules by means of case research from main tech firms, discover 27 open-ended interview questions with community-contributed solutions, and uncover sources for constructing manufacturing ML methods.
// 4. A Information to Manufacturing Stage Deep Studying
Repository: alirezadir/Manufacturing-Stage-Deep-Studying
This repository supplies a information to production-level deep studying methods design.
You’ll study the 4 key levels: mission setup, information pipelines, modeling, and serving, by means of sensible sources and real-world case research from ML engineers at main tech firms.
The information contains 27 open-ended interview questions with community-contributed solutions.
// 5. Deep Studying In Manufacturing Guide
Repository: The-AI-Summer time/Deep-Studying-In-Manufacturing
This repository supplies Deep Studying In Manufacturing, a complete ebook on constructing sturdy ML functions.
You’ll study finest practices for writing and testing DL code, establishing environment friendly information pipelines, serving fashions with Flask/uWSGI/Nginx, deploying with Docker/Kubernetes, and implementing end-to-end MLOps utilizing TensorFlow Prolonged and Google Cloud.
It’s best for software program engineers coming into DL, researchers with restricted software program background, and ML engineers looking for production-ready expertise.
// 6. Machine Studying + Kafka Streams Examples
Repository: kaiwaehner/kafka-streams-machine-learning-examples
This repository demonstrates deploying analytic fashions to manufacturing utilizing Apache Kafka and its Streams API.
You’ll study to combine TensorFlow, Keras, H2O, and DeepLearning4J fashions into scalable streaming pipelines; implement mission-critical use circumstances like flight delay prediction and picture recognition with unit checks; and leverage Kafka’s ecosystem for sturdy, production-ready ML infrastructure.
// 7. NVIDIA Deep Studying Examples for Tensor Cores
Repository: NVIDIA/DeepLearningExamples
This repository supplies state-of-the-art deep studying examples optimized for NVIDIA Tensor Cores on Volta, Turing, and Ampere GPUs.
You’ll study to coach and deploy high-performance fashions throughout pc imaginative and prescient, NLP, recommender methods, and speech utilizing frameworks like PyTorch and TensorFlow; leverage automated combined precision, multi-GPU/node coaching, and TensorRT/ONNX conversion for optimum throughput.
// 8. Superior Manufacturing Machine Studying
Repository: EthicalML/awesome-production-machine-learning
This repository curates a complete checklist of open supply libraries for manufacturing machine studying.
You’ll study to navigate the MLOps ecosystem by means of categorized software listings, uncover options for deployment, monitoring, and scaling utilizing the built-in search toolkit, and keep present with month-to-month neighborhood updates protecting the whole lot from AutoML to mannequin serving.
// 9. MLOps Course
Repository: GokuMohandas/mlops-course
This repository supplies a complete MLOps course taking you from ML experimentation to manufacturing deployment.
You’ll study to construct production-grade ML functions following software program engineering finest practices; scale workloads utilizing Python, Docker, and cloud platforms; implement end-to-end pipelines with experiment monitoring, orchestration, mannequin serving, and monitoring; and create CI/CD workflows for steady coaching and deployment.
// 10. MLOPs Primer
Repository: dair-ai/MLOPs-Primer
This repository curates important MLOps sources that will help you upskill in deploying ML fashions.
You’ll study the MLOps tooling panorama, data-centric AI rules, and manufacturing system design by means of blogs, books, and papers; uncover neighborhood sources and programs for hands-on follow; and construct a basis for creating scalable, accountable machine studying infrastructure.
Repository Map
Right here’s a fast comparability desk that will help you perceive how every repository matches into the broader ML deployment ecosystem:
| Repository | Sort | Major Focus |
|---|---|---|
| DataTalksClub/mlops-zoomcamp | Structured course | Finish-to-end MLOps: coaching → deployment → monitoring with a 9-week roadmap |
| GokuMohandas/Made-With-ML | Manufacturing ML course | Manufacturing-grade ML methods, CI/CD, scalable serving |
| chiphuyen/machine-learning-systems-design | Booklet + Q&A | ML methods design fundamentals, trade-offs, interview-style situations |
| alirezadir/Manufacturing-Stage-Deep-Studying | Information | Manufacturing-level DL setup, information pipelines, modeling, serving |
| The-AI-Summer time/Deep-Studying-In-Manufacturing | Guide | Sturdy DL functions: testing, pipelines, Docker/Kubernetes, TFX |
| kaiwaehner/kafka-streams-machine-learning-examples | Code examples | Actual-time/streaming ML with Apache Kafka & Kafka Streams |
| NVIDIA/DeepLearningExamples | Excessive-perf examples | GPU-optimized coaching & inference on NVIDIA Tensor Cores |
| EthicalML/awesome-production-machine-learning | Superior checklist | Curated instruments for deployment, monitoring, and scaling |
| GokuMohandas/mlops-course | MLOps course | Experimentation → manufacturing pipelines, orchestration, serving, monitoring |
| dair-ai/MLOPs-Primer | Useful resource primer | MLOps fundamentals, data-centric AI, manufacturing system design |
Abid Ali Awan (@1abidaliawan) is a licensed information scientist skilled who loves constructing machine studying fashions. Presently, he’s specializing in content material creation and writing technical blogs on machine studying and information science applied sciences. Abid holds a Grasp’s diploma in know-how administration and a bachelor’s diploma in telecommunication engineering. His imaginative and prescient is to construct an AI product utilizing a graph neural community for college students fighting psychological sickness.
