Mastering ML Deployment: FastAPI, Pydantic, and Containerization

Description

In this video, we move beyond the Jupyter Notebook to explore the crucial step of Machine Learning Deployment. We’ll use FastAPI, a modern Python framework, to package trained models behind robust APIs. What you will learn: The importance of model deployment in real-world applications. Difference between Batch, Online, and Streaming inference. How to use Pydantic for rigorous input validation. Containerizing your ML application with Docker for cloud readiness. Best practices for production: Health endpoints, logging, and model versioning. Whether you are an MCA/MSc student or a researcher working with Scikit-learn, TensorFlow, or PyTorch, this guide provides a scalable workflow for your data science projects.