🚀 From Design Patterns to Web APIs

Week 4, Day 2: Connecting Yesterday's Patterns to Today's APIs

📚 Your Learning Journey

W3D4
Advanced OOP

→

Classes, Inheritance, Polymorphism

You learned to organize code into reusable classes, create inheritance hierarchies, and use polymorphism to write flexible code.

W4D1
Design Patterns

→

Factory, Strategy, Observer, Singleton

You mastered architectural patterns that solve common software design problems. You built ML systems using these proven templates.

W4D2
APIs (TODAY)

→

REST APIs with FastAPI

Today: You'll expose your ML systems to the world through web APIs, making your code accessible to any application, anywhere.

W4D4
Monitoring

→

Logging & Observability

Coming: You'll add eyes and ears to your APIs, learning how to monitor, debug, and maintain production systems.

🌉 The Bridge: Patterns Meet APIs

Yesterday you learned HOW to architect code.
Today you learn how to EXPOSE that code to the world.

🏭 Factory Pattern

Creating Different Models via API

Yesterday: Created different ML models using Factory pattern

Today: Let users create models through API endpoints

# API lets users choose model type
@app.post("/models/create")
def create_model(type: str):
    # Factory pattern!
    model = MLModelFactory
        .create_model(type)
    return {"id": model.id}
                    

🎯 Strategy Pattern

Switching Algorithms via API

Yesterday: Swapped training strategies dynamically

Today: API users pick their strategy with a parameter

# Let users choose processing
@app.post("/predict")
def predict(
    strategy: str,
    data: list
):
    if strategy == "fast":
        algo = FastStrategy()
    else:
        algo = AccurateStrategy()
    return algo.predict(data)
                    

👁️ Observer Pattern

API Monitoring & Logging

Yesterday: Observers logged ML model events

Today: Automatically monitor every API request

# Observer tracks API usage
@app.post("/train")
def train(config: dict):
    # Wrapped with observer
    model = MonitoredModel(
        base_model
    )
    model.train(data)
    # Auto-logged: accuracy,
    # time, errors, etc.
    return {"accuracy": 0.89}
                    

🔒 Singleton Pattern

Shared API Configuration

Yesterday: One config instance for entire app

Today: All API endpoints share same config

# Config singleton accessed
# by all API endpoints
config = ConfigManager()

@app.get("/settings")
def get_settings():
    # Same config everywhere
    return config.get_all()

@app.post("/train")
def train():
    # Uses same config
    params = config.get("ml")
                    

🌍 Real-World: How Companies Use Pattern-Based APIs

The patterns you learned yesterday power the APIs used by millions of people every day.

🎵 Spotify

Factory: Creates different recommendation models

Strategy: Switches between recommendation algorithms

Observer: Tracks what users listen to

API: GET /recommendations

🚗 Uber

Factory: Creates different vehicle types

Strategy: Different pricing algorithms (surge, flat, etc.)

Observer: Real-time driver location updates

API: POST /rides/request

📺 Netflix

Factory: Creates different video quality streams

Strategy: Recommendation strategies by context

Observer: Tracks viewing habits

API: GET /personalized-content

🤖 OpenAI

Factory: Creates different AI models (GPT-3, GPT-4)

Strategy: Temperature and sampling strategies

Observer: Usage tracking and rate limiting

API: POST /chat/completions

💬 Chat Activity: Share Your Experience!

Question 1: Think about an app you used TODAY. What API calls do you think it made?

Examples: Checking weather, loading social media feed, getting directions, making a payment...

Question 2: From yesterday's W4D1 patterns, which one do you think will be MOST useful for APIs? Why?

Think about: Factory (creating things), Strategy (choosing algorithms), Observer (monitoring), Singleton (shared config)

Bonus Question: If you could build an API for anything, what would it do?

Dream big! Recipe recommendations? Fitness tracking? Game matchmaking? Weather predictions?

🎯 What You'll Learn Today

⚡ FastAPI Basics

Build your first API in minutes. Automatic documentation. Type safety. Python magic!

🌐 REST Principles

Learn the rules of good API design. URLs, HTTP methods, status codes - the universal language of the web.

🔗 Pattern Integration

Use yesterday's patterns in real APIs. Factory for endpoints, Strategy for algorithms, Observer for logging.

🤖 ML Model APIs

Expose your ML models to the world. Train models via API. Make predictions. Monitor performance.

🔒 Security Basics

Add authentication. Protect endpoints. Validate inputs. Rate limiting. Production-ready features.

📖 Documentation

Interactive API docs. Test endpoints in the browser. OpenAPI/Swagger standards. Make APIs developers love.

💡 The Big Picture

Yesterday you learned to architect systems using patterns.
Today you learn to share those systems through APIs.
Tomorrow the world can use what you built.

🚀 You're becoming a full-stack ML engineer! 🚀