FastAPI Model Serving for ML | DeployBytes 8 - The Big Picture (Musical)

Real MLOps, step by step. Part 8 serves the trained 7-encoder model over HTTP with FastAPI: a thin app on Uvicorn with /health and /predict, a lazy-loading singleton so the weights load once, a one-line Prometheus /metrics endpoint, and a container healthcheck and restart policy that keep the serving service self-healing. Why this series exists: This video is part of a series created to make central concepts easier to understand and hold onto. It was built with accessibility in mind, particularly for learners with learning differences, so the pace and style are kept simple and supportive. There may be a few mispronunciations in the spoken narration, and occasionally a small mistake in the content. These do not take away from the purpose of the series, which is simply to help the learning stick. Work with me:   / sandeep-grover-b3192a16   Productionizing a real 7-encoder multimodal Rakuten classifier (84,916 products, weighted-F1 0.9147), from git to Kubernetes. Series stack (all 12 episodes): Python, cookiecutter-data-science, Git/GitHub, Docker, Docker Compose, PostgreSQL, MinIO (S3), MLflow, DVC, FastAPI, Prometheus, pytest, Nginx, Apache Airflow, Kubernetes, Argo Rollouts, PyTorch, Hugging Face Transformers. Music: Suno AI | Slides: NotebookLM #MLOps #FastAPI #ModelServing #ModelDeployment #Prometheus #Docker #MachineLearning #Python #DevOps #MLEngineering