Jan 16
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Instructor in this video - Dhanesh Malviya Code link - https://drive.google.com/file/d/1TLjZTj4z8QSbBXX2ew4cavlJyL6TmLUv/view?usp=sharing Data science playlist - https://www.youtube.com/playlist?list=PLaldQ9PzZd9qPYGj4aWUXitBlfWz72e9m Data Science Course - https://sheryians.com/courses/courses-details/Data%20Science%20and%20Analytics%20with%20GenAI our Instagram - https://www.instagram.com/sheryians.ai If you’re learning FastAPI and want more than just basic examples, this tutorial is for you. In this video, I’ve covered FastAPI from scratch to deployment, focusing on how APIs are actually built and used in real projects. The goal of this course is simple: help beginners understand FastAPI deeply and confidently enough to build their own production-ready APIs. We start with the core fundamentals and gradually move towards building a complete backend system. To make the logic clear and beginner-friendly, we use a local JSON database and implement full CRUD operations, request validation, and proper API structure. Once the foundation is strong, we take things a step further by integrating a Machine Learning model into FastAPI. We build a Car Price Prediction API, expose the ML model through endpoints, and connect it with a Streamlit frontend so you can see how backend, ML, and UI work together in a real application. Finally, we deploy everything: FastAPI backend is deployed on Render Streamlit application is deployed on Streamlit Cloud By the end of this tutorial, you’ll understand not just how FastAPI works, but how to think like a backend developer while building APIs. What this tutorial covers FastAPI basics and project setup Structuring FastAPI projects for larger applications Request and response validation using Pydantic CRUD operations with a local JSON database API error handling and status codes Connecting FastAPI with a Machine Learning model Building a Car Price Prediction API Creating a frontend using Streamlit End-to-end integration of Backend, ML, and UI Deploying FastAPI on Render Deploying Streamlit apps on Streamlit Cloud Who should watch this video Beginners starting with FastAPI Python developers moving into backend development Data Science and ML students who want to deploy models Anyone who wants a clear, practical understanding of API development Most FastAPI tutorials stop after explaining endpoints. This one focuses on real implementation, real structure, and real deployment. If you watch this tutorial till the end, you’ll have everything you need to start building FastAPI projects on your own with confidence. Tech stack used FastAPI, Python, Pydantic, JSON, Machine Learning, Streamlit, Render If this tutorial helps you, consider liking the video, sharing it with others, and subscribing for more backend, API, ML, and MLOps content.