Kyun Seekhna Zaroori Hai?
Capstone project se sab kuch apply karo � complete AI system banao with API, Docker, monitoring, security. Ye aapka final project hai jo dikhata hai ki aapne poora AI Engineering course master kar liya hai!
Prerequisite:
Ye lesson Cost Optimization ke baad seekhna best hai. Saare previous concepts samajhna zaroori hai.
Complete AI System Architecture
Client App
API Security
FastAPI Server
AI Model
Monitoring
Core Concepts
FULL SYSTEM
Complete pipeline � data se lekar deployment tak. Har component properly integrated hona chahiye.
PRODUCTION
Real deployment ready. Sirf code likhna kaafi nahi � reliability, scalability zaroori hai.
SCALING
Growing users handle karo. 10 se 10,000 users tak system smoothly chalna chahiye.
MAINTENANCE
Ongoing support � updates, bug fixes, performance optimization. System continuously improve hota rahe.
Production AI System Components
Ek complete production AI system mein ye 5 essential components hote hain:
API Layer
FastAPI endpoints jo models ko accessible banaye
Security
API keys, authentication, rate limiting
Caching
LRU cache se repeated predictions fast karo
Monitoring
Logs, metrics, health checks track karo
Health Checks
System status verify karo � API, model, cache sab healthy?
Complete AI System Code
Ye ek complete production-ready AI system hai with all components. Har section samajhte hain:
# Complete AI Engineering System
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from functools import lru_cache
import time
import logging
app = FastAPI(title="DSWallah AI System")
# Monitoring
logger = logging.getLogger("ai-system")
# Security
API_KEYS = {"dswallah-key-123"}
# Caching
@lru_cache(maxsize=1000)
def predict(text: str):
time.sleep(0.1) # Simulate model
return {"sentiment": "positive", "confidence": 0.9}
@app.post("/api/predict")
async def api_predict(text: str, api_key: str):
if api_key not in API_KEYS:
raise HTTPException(401, "Invalid API key")
start = time.time()
result = predict(text)
latency = (time.time() - start) * 1000
logger.info(f"Prediction: {latency:.2f}ms")
return result
@app.get("/api/health")
async def health():
return {"status": "healthy", "cache_size": predict.cache_info().currsize}
@app.get("/api/metrics")
async def metrics():
return predict.cache_info()POST request bhejo:
http://localhost:8000/api/predict?text=hello&api_key=dswallah-key-123Health check:
http://localhost:8000/api/healthMetrics:
http://localhost:8000/api/metrics
Interactive Editor
Apna complete AI system build karo � code likho aur test karo:
Exercise
Ye question aapko samajhna chahiye complete AI system ke liye: