Chapter 6 – Real-Time Prediction Systems (Django AI) | Practical AI/ML Course

Chapter 6: Real-Time Prediction Systems (Django AI)

This chapter focuses on building real-time prediction systems.
Here, AI is not just a trained model — it becomes a live prediction engine
that continuously receives data and produces instant predictions.

Real-time AI systems are used in:
finance, healthcare, e-commerce, security, IoT, automation, smart cities, and enterprise systems.
This chapter teaches how to design and implement live AI prediction pipelines using Django.

⭐ What is a Real-Time Prediction System?

• Continuous data input
• Live processing
• Instant AI inference
• Real-time decisions
• Immediate system response

⭐ Real-Time Prediction Architecture

Live Input → Data Pipeline → AI Model → Prediction Engine → Django API → System Action

⭐ Prediction Pipeline Flow

Input → Preprocessing → Model → Prediction → Decision → Output

⭐ Simple Real-Time Prediction Logic

def prediction_engine(value):
    processed = value * 2 + 10
    
    if processed > 100:
        return "High Risk"
    else:
        return "Low Risk"

⭐ Django Real-Time Prediction API

Basic real-time prediction API using Django:

# views.py
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json

@csrf_exempt
def realtime_predict(request):
    if request.method == "POST":
        data = json.loads(request.body)
        value = data.get("value")

        processed = value * 2 + 10

        if processed > 100:
            result = "High Risk"
        else:
            result = "Low Risk"

        return JsonResponse({
            "input": value,
            "prediction": result
        })

⭐ URL Configuration

# urls.py
from django.urls import path
from .views import realtime_predict

urlpatterns = [
    path("realtime-predict/", realtime_predict),
]

⭐ Live Simulation Script

Simulating live data feed:

import time
import random
import requests

while True:
    value = random.randint(1, 100)
    response = requests.post(
        "http://127.0.0.1:8000/realtime-predict/",
        json={"value": value}
    )
    print("Input:", value, "Prediction:", response.json())
    time.sleep(2)

⭐ Model-Based Real-Time Prediction (Django)

# views.py
import tensorflow as tf
import numpy as np

model = tf.keras.models.load_model("model.h5")

@csrf_exempt
def realtime_model_predict(request):
    if request.method == "POST":
        data = json.loads(request.body)
        values = data.get("data")

        arr = np.array(values).reshape(1, -1)
        pred = model.predict(arr)[0][0]

        return JsonResponse({
            "prediction": float(pred)
        })

⭐ Real-Time AI Use Cases

• Fraud detection
• Stock prediction systems
• Traffic prediction
• Medical risk prediction
• Recommendation engines
• Smart automation

⭐ Real-Time AI Design Principles

• Low latency
• Fast inference
• Stable pipelines
• Scalable APIs
• Fault tolerance

⭐ Mini Practical Task

Build a Django real-time prediction system that:

• Receives live data
• Processes input
• Runs AI logic/model
• Returns prediction instantly

# views.py
@csrf_exempt
def score_predict(request):
    if request.method == "POST":
        data = json.loads(request.body)
        score = data.get("score")

        risk = score * 2 + 20

        if risk > 120:
            result = "High Risk"
        else:
            result = "Safe"

        return JsonResponse({"result": result})

📌 Chapter Outcome

• Build real-time AI systems
• Create live prediction engines
• Deploy real-time AI services
• Integrate AI into systems
• Design scalable AI prediction pipelines

📌 Core Principle

Static models are not AI systems.
Real-time prediction is real AI.
Live intelligence builds real products.