Overview What You Will Learn Curriculum Instructor

Course Overview

FastAPI and Docker provide a practical foundation for deploying machine learning models as scalable, production-ready applications. FastAPI enables high-performance API development, while Docker simplifies application packaging, portability, testing, and deployment across environments. This course begins with machine learning fundamentals and model deployment, then covers Streamlit, FastAPI application development, model serving, production preparation, Docker containerization, testing, and CI/CD. It progresses through hands-on projects involving Heroku and Microsoft Azure, including cloud deployment, scaling, monitoring, and end-to-end deployment of custom ML models. You will gain practical skills to build, containerize, test, deploy, monitor, and scale machine learning models using FastAPI and Docker across local and cloud environments.

What You Will 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.

Program Curriculum

  • Basics of Machine Learning
  • Machine Learning (ML) Model Deployment
  • Setting Up the Development Environment
  • Chapter 1 Quiz

  • Loading the Data for the ML Model
  • Training the ML Model
  • Evaluating and Saving the ML Model
  • Chapter 2 Quiz

  • Introduction to Streamlit
  • Writing Your First Streamlit Application
  • Deploy ML Model with Streamlit
  • Chapter 3 Quiz

  • Overview of FastAPI
  • Creating a Simple API
  • Defining Routes and Endpoints
  • Managing Requests and Response Bodies
  • Chapter 4 Quiz

  • Setting Up FastAPI for the ML Project
  • Defining Routes and Endpoints for Model Prediction
  • Testing FastAPI Endpoints Locally
  • Chapter 5 Quiz

  • Preprocessing and Preparation of Wine Quality Data
  • Adding Pivot Tables
  • Building the FastAPI
  • Testing FastAPI Endpoints Locally
  • Chapter 6 Quiz

  • Preload the Model for Latency Reduction
  • Implementing Request Handling, Error Handling, and Logging
  • Configuring Environment Variables
  • Write a Simple Frontend for Model Serving
  • Chapter 7 Quiz

  • Overview of Docker
  • Writing a Dockerfile
  • Building and Running a Docker Image
  • Chapter 8 Quiz

  • Writing a Dockerfile to Containerize the FastAPI App
  • Building and Running Docker Images Locally for Testing
  • Implementing Unit and Integration Tests for Model API Endpoints
  • Chapter 9 Quiz

  • Training and Saving the Model
  • Building the FastAPI
  • Testing the Endpoints Locally
  • Develop Simple HTML for Fontend for Model Serving
  • Configure FastAPI to Communicate with Frontend
  • Prepare the Application for Production
  • Dockerizing the FastAPI Production
  • Implementing Unit and Integration Tests for Model API Endpoints
  • Chapter 10 Quiz

  • Overview of Heroku
  • Deploy ML Model on Heroku
  • Setting Up a CI/CD Pipeline with GitHub Actions
  • Scaling and Monitoring with Heroku
  • Chapter 11 Quiz

  • Deploy Iris Classification ML Model on Heroku
  • Setting Up a Deployment Pipeline with CI/CD
  • Scaling and Monitoring Iris Species Classifier with Heroku
  • Chapter 12 Quiz

  • Overview of Microsoft Azure
  • Deploy ML to Microsoft Azure
  • Monitoring Application with Azure
  • Chapter 13 Quiz

  • Preprocessing and Preparation of Data
  • Training and Saving the Model
  • Creating the FastAPI Application
  • Creating a Simple Frontend
  • Preparing the Application for Production
  • Containerizing the Application with Docker
  • Writing Unit and Integration Tests
  • Deploy to Cloud Provider
  • Setting Up Continuous Integration/Continuous Deployment (CI/CD)
  • Chapter 14 Quiz
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Instructor

Meta Brains

Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for Coding, Finance & Excel. We bring together both professional and educational experiences to create world-class training programs accessible to everyone. Currently, we're focused on the next great revolution in computing: The Metaverse. Our ultimate objective is to train the next generation of talent so we can code & build the metaverse together!

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