Deploy ML Model in Production with FastAPI and Docker

Deploy ML Model in Production with FastAPI and Docker

Learn ML deployment using FastAPI, Docker, CI/CD, and Cloud platforms

What you'll learn

• Deploy machine learning models in production using FastAPI and Docker.
• Create APIs for ML models using FastAPI with optimized endpoints.
• Containerize ML applications with Docker for scalable deployments.
• Set up CI/CD pipelines for automated deployment and testing.
• Train, evaluate, and save ML models, focusing on real-world datasets.
• Deploy ML models to cloud platforms like Heroku and Microsoft Azure.
• Build and integrate a simple frontend for ML model APIs.
• Implement logging, error handling, and request handling in APIs.

Stop building models that live and die in notebooks. It's time your ML creations actually see the light of day.

Transform your machine learning projects from academic exercises to production-ready applications with this comprehensive, hands-on course. Master the entire ML deployment pipeline using industry-standard tools that employers are actively seeking.

In this practical journey, you'll build real-world ML systems that deliver actual business value. Starting with fundamental ML concepts, you'll quickly progress to crafting robust APIs with FastAPI, containerizing applications with Docker, and deploying scalable solutions across multiple cloud platforms including Heroku and Microsoft Azure.

Deploy ML Model in Production with FastAPI and Docker