📖 Course Description:

Unlock the full potential of FastAPI in this comprehensive course that takes you from API fundamentals to building, testing, and deploying production-ready machine learning applications. Whether you're a backend developer, data scientist, or aspiring full-stack engineer, this course is designed to give you hands-on experience with FastAPI, modern API development practices, database integration, ML model deployment, and system performance optimization.

🧱 What You’ll Learn:

✅ Section 1: Introduction to APIs

• Grasp the core concepts of APIs, their types, protocols, components, and lifecycle.
• Understand how authentication and authorization work in real-world applications.

⚡ Section 2: FastAPI Fundamentals

• Get introduced to FastAPI’s architecture and key features.
• Build your first web application using FastAPI.
• Compare FastAPI with other web frameworks like Flask and Django.

🗃️ Section 3: Database Integration

• Learn how to integrate relational databases using SQLAlchemy.
• Build a full-fledged CRUD app with modular project structure and clean code practices.

🤖 Section 4: Machine Learning Integration

• Learn how to serialize ML models using Pickle, Joblib, and Keras.
• Understand schema design for handling model input/output.
• Serve your trained models via FastAPI endpoints and handle batch predictions.

🚀 Section 5: Advanced FastAPI Concepts

• Explore FastAPI’s middleware system and dependency injection mechanism.
• Implement secure authentication using JWT and manage API keys.
• Learn best practices for designing scalable and maintainable APIs.

🧪 Section 6: Testing & Debugging

• Master unit, integration, and end-to-end testing of FastAPI applications.
• Learn to mock ML models, handle common errors, and debug using logging and curl.

⚙️ Section 7: Performance Optimization & Monitoring

• Optimize your APIs using caching with Redis for predictions, DB queries, and external calls.
• Profile and benchmark your APIs using industry tools like cProfile, line-profiler, and Locust.
• Set up monitoring with Prometheus and Grafana, and visualize metrics via Docker integration.

🧠 Section 8: Capstone Project

• Apply everything you’ve learned by building and deploying a real-world ML-powered API project.
• Integrate caching, authentication, and monitoring.
• Deploy the full project using GitHub, Docker, Redis, and Render.

🛠️ Tools & Technologies Covered:

• FastAPI, SQLAlchemy, Redis, Docker, Prometheus, Grafana
• Pickle, Joblib, Keras, Locust
• PostgreSQL, Uvicorn, Pytest

🎯 Who Should Enroll:

• Python developers wanting to learn modern API frameworks.
• Data scientists and ML engineers looking to deploy ML models.
• Backend engineers exploring high-performance web frameworks.
• Anyone interested in building and scaling full-stack ML applications.

Course Duration - 24 Hours