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Model Deployment

Practical guides for deploying ML models to production - from simple web apps to enterprise-scale systems.

Deployment Options Overview

There's no one-size-fits-all deployment strategy. Here's when to use what:

🌐 Web API Deployment

When to use: Real-time predictions, web applications, mobile apps - FastAPI + Docker - Flask for simple cases - Load balancing strategies

⚡ Batch Processing

When to use: Large datasets, scheduled predictions, ETL pipelines - Apache Spark - Airflow workflows - Cloud batch services

🔄 Streaming Predictions

When to use: Real-time events, IoT data, live recommendations - Kafka + ML models - Stream processing frameworks - Edge deployment

Deployment Patterns

Pattern 1: Simple API Service

Perfect for MVPs and small-scale applications.

# Example FastAPI deployment
from fastapi import FastAPI
import joblib

app = FastAPI()
model = joblib.load("model.pkl")

@app.post("/predict")
def predict(data: dict):
    prediction = model.predict([data])
    return {"prediction": prediction.tolist()}

Pattern 2: Microservices Architecture

For complex applications with multiple models.

  • Model serving containers
  • API gateway
  • Service discovery
  • Circuit breakers

Pattern 3: Serverless Deployment

Cost-effective for sporadic usage patterns.

  • AWS Lambda
  • Google Cloud Functions
  • Azure Functions

Scaling Considerations

Horizontal vs Vertical Scaling

  • When to scale up vs scale out
  • Auto-scaling strategies
  • Cost optimization

Caching Strategies

  • Redis for model caching
  • Feature caching patterns
  • Result caching

Model Versioning in Production

  • Blue-green deployments
  • Canary releases
  • Rollback strategies

Hands-On Tutorials

Each section includes: - Step-by-step deployment guides - Docker configurations - Monitoring setup - Performance optimization tips

These deployment patterns are battle-tested in production environments. Learn from real implementations, not just theory.