FastAPI Mastery for Data Science Applications

Day 1: Introduction to FastAPI and Data Science Integration

Learning Objectives:

• Understand the core principles and features of FastAPI.
• Explore the importance of FastAPI in modern data science applications.
• Set up the development environment for working with FastAPI.
• Create and run your first FastAPI application.

Agenda:

1. Overview of FastAPI: A Modern Web Framework.
2. FastAPI's Advantages for Data Science Projects.
3. Setting Up the Development Environment: Tools and Requirements.
4. Writing and Running Your First FastAPI App.
5. Basics of HTTP Methods and API Endpoints.

Day 2: API Design and Data Handling

Learning Objectives:

• Learn how to define and manage API routes.
• Handle input validation and data serialization effectively.
• Work with JSON data and request models.

Agenda:

1. Structuring Your API Endpoints.
2. Defining and Using Path and Query Parameters.
3. Input Validation with Pydantic Models.
4. Working with JSON Data.
5. Creating and Using Response Models.

Day 3: Building Dynamic and Interactive APIs

Learning Objectives:

• Develop dynamic APIs for data processing tasks.
• Handle complex data operations in the backend.
• Understand asynchronous programming and its application in APIs.

Agenda:

1. Building Dynamic API Endpoints for Data Processing.
2. Handling Large Data Inputs in APIs.
3. Introduction to Asynchronous Programming in Python.
4. Leveraging FastAPI for Concurrent Request Handling.
5. Hands-On: Building APIs for Common Data Science Use Cases.

Day 4: Integrating Data Science Models with FastAPI

Learning Objectives:

• Deploy machine learning models using FastAPI.
• Expose predictive models through API endpoints.
• Optimize and test APIs for performance.

Agenda:

1. Loading and Managing Machine Learning Models in APIs.
2. Designing Endpoints for Model Inference.
3. Optimizing API Performance for High Traffic.
4. Testing and Debugging Your FastAPI Application.
5. Hands-On: Deploy a Pre-trained Model Using FastAPI.

Day 5: Advanced Features and Deployment

Learning Objectives:

• Implement authentication and authorization mechanisms.
• Explore advanced FastAPI features like middleware and dependency injection.
• Learn deployment strategies for FastAPI applications.

Agenda:

1. Adding Authentication and Authorization to Your APIs.
2. Using Middleware for Pre- and Post-Processing.
3. Dependency Injection for Modular Code Design.
4. Deployment Strategies: Docker, Cloud, and CI/CD Pipelines.
5. Final Project: Build and Deploy a Data Science Application.

Instructor Kit

(PPTX/PDF of Slides + Optional Instructor Notes)
Comprehensive slide deck with detailed content covering all modules, plus optional instructor notes to enhance teaching effectiveness.

Student Kit / Handout

(with Free Branding)
Professionally designed handouts for students, including all essential course information and customizable branding options for your organization.

Course Agenda / Outline

Detailed day-by-day course agenda and outline, ensuring smooth course delivery and a structured learning experience for students.

Study Guide

A concise guide summarizing key concepts and topics covered in the course, perfect for post-course review and exam preparation.

FAQ

Answers to commonly asked questions about the course content, delivery, and labs to support instructors and students.

Briefing Doc

A high-level document summarizing the course objectives, target audience, and key learning outcomes, ideal for internal use and marketing.

Sales Enablement Kit for IT Training Sales Engineers

(Additional Fee)
Exclusive toolkit designed for IT training sales teams, including pitch decks, objection handling, and ROI documentation to support course sales.

Course AI GPT

(Course Assistant GPT so students can talk to the course materials!)
A cutting-edge AI-driven assistant that allows students to interact with course content, ask questions, and receive instant feedback.

Optional Podcast

(of the entire course or for each individual module)
Engaging audio content covering the entire course or individual modules, perfect for on-the-go learning or reinforcement.

Lab Guide

(Lab Environments are additional and can be found at CourseLabs.io)
Step-by-step lab guide to support hands-on learning, with lab environments available separately at CourseLabs.io.

Lab Files

(If you choose to host your own lab environment)
All necessary files and instructions for setting up and running labs in your own environment, offering flexibility in deployment.

Python: 3.10.8 (FastAPI compatible with 3.7+).

Pyenv: 2.3.6 (manages Python versions).

FastAPI: Latest stable version.

Uvicorn: Latest stable version (ASGI server).

HTTPie: Latest stable version (API testing).

Docker: Latest stable version (containerization).

Prometheus: Latest stable version (metrics collection).

Grafana: Latest stable version (dashboard visualization).

Loguru: Latest stable version (logging).

Course Overview

Our Building Data Science Applications with FastAPI course offers a dynamic blend of theory and practical application, perfect for learners aiming to master modern data science tools and techniques. This course is designed to provide a comprehensive learning experience with 50% lecture and 50% hands-on labs, ensuring participants gain real-world, applicable skills.

Course Objectives

By the end of this course, participants will:

• Understand the fundamentals of FastAPI for building scalable applications.
• Develop and deploy production-ready APIs for data science models.
• Integrate asynchronous programming for optimal performance.
• Build and customize data-driven applications using Python.
• Master best practices in deploying and managing APIs in real-world environments.

Learning Objectives

Participants will:

• Explore key FastAPI concepts such as routing, request handling, and data validation.
• Gain hands-on experience in integrating machine learning models into applications.
• Learn to optimize APIs for speed, reliability, and scalability.
• Use tools like Docker, Prometheus, and Grafana for monitoring and containerization.
• Understand error tracking and logging using Sentry and Loguru.

Who Should Attend?

This course is designed for:

• Software Developers: Looking to build scalable APIs and integrate data science models into applications.
• Data Scientists: Eager to deploy their models into real-world systems.
• Tech Enthusiasts: Interested in mastering the FastAPI framework for advanced application development.

Course Customization

We understand that every organization has unique training needs. That’s why this course can be customized into 5, 4, 3, 2, or even 1-day formats to suit your schedule and objectives.

• Cost: $40 per student per day
• Flexibility: Tailor the content to match your audience's expertise and learning pace.

Ready to empower your team with cutting-edge skills in FastAPI and data science application development? Contact us today to learn more!