Kubernetes Tutorial: Deploy Machine Learning Models with Docker and FastAPI

Learn step by step how to deploy machine learning models on Kubernetes — from building a FastAPI service and packaging it with Docker to deploying it on a Kubernetes cluster and scaling it with Horizontal Pod Autoscaling (HPA).

This workshop is part of the Machine Learning Zoomcamp, a free course on machine learning engineering and MLOps. You’ll learn practical Kubernetes deployment workflows used by ML and DevOps teams in production.

What you’ll learn
✅ How Kubernetes works for ML model deployment
✅ Setting up a local Kubernetes cluster with Kind (Kubernetes in Docker)
✅ Building and serving a FastAPI app for ML inference
✅ Creating and managing Kubernetes deployments and services
✅ Packaging your model in Docker for containerized deployment
✅ Adding health checks and horizontal pod autoscaling (HPA)
✅ Best practices for scalable and reliable ML infrastructure

Whether you're a data scientist, ML engineer, or DevOps learner, this hands-on Kubernetes tutorial will teach you how to move models from notebooks to production-ready environments.

🔗 Resources
- 💻 Code for this workshop: https://github.com/alexeygrigorev/workshops/tree/main/mlzoomcamp-k8s
- 📘 Join the free ML Zoomcamp course: https://github.com/DataTalksClub/machine-learning-zoomcamp

🧠 Tools & Technologies
- FastAPI
- Docker
- Kubernetes (K8s)
- Kind (Kubernetes in Docker)
- ONNX Runtime
- PyTorch

⏱️ Chapters
* 0:00 Intro and course context
- 5:07 Start of workshop: Environment — GitHub Codespaces
- 6:00 Required tools — Docker, Kind, kubectl
- 7:12 Local cluster setup — Kind (Kubernetes in Docker)
- 7:37 Service goal — FastAPI for clothing classifier model
- 9:13 Why Kubernetes — industry standard for ML deployment
* 10:28 Dockerizing the app and local run
* 46:08 Kubernetes concepts — Pods Deployments Services
* 50:34 Deployment YAML — replicas image container port
* 54:48 Readiness and liveness probes — /health
* 56:41 Creating Kind cluster
* 58:46 Loading local image into Kind
* 59:17 Applying deployment with kubectl
* 1:01:30 Creating Service and load balancing
* 1:05:48 Port-forward for local access
* 1:08:36 Installing Metrics Server
* 1:09:55 HPA configuration — min 2 max 5 target 50% CPU
* 1:12:03 Load test initiation
* 1:13:48 Autoscaling observed — 2 to 4 replicas
* 1:24:02 Wrap-up

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Connect with Alexey
- Twitter - https://twitter.com/Al_Grigor
- Linkedin - https://www.linkedin.com/in/agrigorev/

Check our free online courses:
- ML Engineering course - http://mlzoomcamp.com
- Data Engineering course - https://github.com/DataTalksClub/data-engineering-zoomcamp
- MLOps course - https://github.com/DataTalksClub/mlops-zoomcamp
- LLM course - https://github.com/DataTalksClub/llm-zoomcamp
- Open-source LLM course: https://github.com/DataTalksClub/open-source-llm-zoomcamp
- AI Dev Tools course: https://github.com/DataTalksClub/ai-dev-tools-zoomcamp

👉🏼 Read about all our courses in one place - https://datatalks.club/blog/guide-to-free-online-courses-at-datatalks-club.html

👋🏼 Support/inquiries
If you want to support our community, use this link - https://github.com/sponsors/alexeygrigorev

If you’re a company, reach us at alexey@datatalks.club

#MachineLearning #Kubernetes #MLOps #MLZoomcamp #FastAPI #Docker #ONNX #PyTorch #MachineLearningEngineering

Видео Kubernetes Tutorial: Deploy Machine Learning Models with Docker and FastAPI канала DataTalksClub ⬛