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Production Engineering

FastAPI for Machine Learning

Complete FastAPI for Machine Learning syllabus path — from basic API endpoints and Starlette routing to Dockerization and advanced Pydantic data validation.

Bridge the gap between local models training and production cloud environments. Expose model estimators via fast RESTful web architectures validated under robust schemas.

6Modules
12+API Endpoints
1ML Project
Module 1

Pre-API Era & Monoliths

Analyze how APIs solve the scaling, database lock, and multi-platform integration limits of monolithic systems.

  • Monolithic Constraints
  • The Decoupled API Solution
  • Restaurant Waiter Analogy
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Module 2

FastAPI Setup & Starlette

Understand the asynchronous design, ASGI servers, Starlette engine, and automatic Swagger OpenAPI schemas.

  • ASGI vs. WSGI engines
  • Asynchronous routes loop
  • First API endpoint launch
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Module 3

HTTP Methods & CRUD Ops

Expose endpoints matching GET, POST, PUT, and DELETE operations using validation schemas and status responses.

  • REST API Specifications
  • Path vs. Query parameters
  • Students Database Simulation
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Module 4

ML Serving Pipeline Project

Build and export Scikit-learn Random Forests, load estimator pipelines, and predict values via Streamlit.

  • Scikit-Learn Model Pipelines
  • Pydantic Input Validations
  • Streamlit Web App Integration
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Module 5

Docker Containerization

Package Python environments into isolated images using custom Dockerfiles, mapping host-container ports.

  • Containers vs. Virtual Machines
  • Dockerfile Instruction layers
  • Docker Hub & AWS Deployments
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Module 6

Pydantic Deep Dive

Enforce data validity before prediction calculations using BaseModel schemas, validators, and dumps.

  • Type Enforcement annotations
  • Decorators and Class Validators
  • Schema Serializations
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FastAPI Syllabus Quick Nav

Monolithic Architecture API Analogy FastAPI Stack First Endpoint HTTP CRUD Parameters ML Pipeline ML Serving Docker Intro Dockerfile Pydantic Models Validators