ML Model Deployment: Flask, FastAPI, Docker, Kubernetes https://WebToolTip.com Published 10/2026 Created by Real Numbers MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All Levels | Genre: eLearning | Language: English | Duration: 51 Lectures ( 1h 28m ) | Size: 332.5 MB Serve models in production: Flask and FastAPI, containers, Kubernetes, autoscaling and the failure modes of each. What you'll learn âš¡ Take a pickled model out of a notebook and serve it over HTTP with input validation, a health route and a version route a product team can rely on âš¡ Build the same service in Flask and FastAPI, explain WSGI versus ASGI as a mechanism, and say honestly which one a given service should use âš¡ Run a load test that means something: hold the payload constant, discard the warm-up, find the knee, and know why a closed-loop generator flatters you âš¡ Containerise the service so the image actually caches, then deploy it with Deployments, Services, ingress, readiness probes and horizontal autoscaling âš¡ Set resource requests and limits deliberately, diagnose an OOMKilled pod that left no application log, and explain why CPU throttles where memory kills âš¡ Reverse a bad release under pressure: code and model artefact together, and recognise the schema change that makes rollback impossible Requirements â— You can write working Python and have trained a model at some point. Deep ML knowledge is not needed - the model arrives already fitted â— Comfort at a command line. You will run processes, build images and apply manifests rather than clicking through portals â— A laptop that runs Python and Docker, plus a local cluster from kind or minikube. CPU-only and offline except one marked serverless lecture â— No prior experience of containers, Kubernetes, gunicorn, nginx or FastAPI. All of them are built up from the primitive