Kubernetes Tutorial: Deploy Machine Learning Models with Docker and FastAPI скачать в хорошем качестве

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/wor... 📘 Join the free ML Zoomcamp course: https://github.com/DataTalksClub/mach... 🧠 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 Connect with DataTalks.Club: Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/... Check other upcoming events - https://lu.ma/dtc-events GitHub: https://github.com/DataTalksClub LinkedIn -   / datatalks-club   Twitter -   / datatalksclub   Website - https://datatalks.club/ Connect with Alexey Twitter -   / al_grigor   Linkedin -   / agrigorev   Check our free online courses: ML Engineering course - http://mlzoomcamp.com Data Engineering course - https://github.com/DataTalksClub/data... MLOps course - https://github.com/DataTalksClub/mlop... LLM course - https://github.com/DataTalksClub/llm-... Open-source LLM course: https://github.com/DataTalksClub/open... AI Dev Tools course: https://github.com/DataTalksClub/ai-d... 👉🏼 Read about all our courses in one place - https://datatalks.club/blog/guide-to-... 👋🏼 Support/inquiries If you want to support our community, use this link - https://github.com/sponsors/alexeygri... If you’re a company, reach us at alexey@datatalks.club #MachineLearning #Kubernetes #MLOps #MLZoomcamp #FastAPI #Docker #ONNX #PyTorch #MachineLearningEngineering