Building an ML-Powered Notification Router on AWS: A Production Architecture Guide

Introduction

Sending notifications at the wrong time is like knocking on someone's door at 3 AM. Even if you have something important to say, timing matters. In this article, I'll walk you through building a production-grade, ML-powered notification routing engine that predicts the optimal send time for each user based on their historical engagement patterns.

The Problem: Notification Fatigue

The Business Challenge

Modern applications send billions of notifications daily. Email, SMS, push notifications, WhatsApp messages—the channels are endless. But here's the problem:

Traditional Approaches (and Why They Fail)

The Technical Challenge

Building a smart notification router requires solving several problems:

1. 1
   Real-time prediction: Decision latency must be <500ms
2. 2
   Personalization: Each user has unique engagement patterns
3. 3
   Scale: Handle millions of events per day
4. 4
   Feedback loops: Model must improve with delivery outcomes
5. 5
   Cost optimization: Keep AWS costs under $500/month for 1M+ events

Solution Architecture

High-Level Design

The system consists of three main flows:

Complete System Architecture

Architecture Principles

Core Components Deep Dive

// services/control-plane/src/main/java/Handler.java
public class Handler implements RequestHandler<APIGatewayV2HTTPEvent,
                                               APIGatewayV2HTTPResponse> {
    private final KinesisClient kinesis;

    @Override
    public APIGatewayV2HTTPResponse handleRequest(...) {
        ObjectMapper json = new ObjectMapper();
        UserEvent userEvent = json.readValue(
            event.getBody(), UserEvent.class
        );

        // Validate and stream to Kinesis
        PutRecordRequest putReq = PutRecordRequest.builder()
            .streamName(streamName)
            .partitionKey(userEvent.getUserId())
            .body(SdkBytes.fromUtf8String(
                json.writeValueAsString(userEvent)
            ))
            .build();

        kinesis.putRecord(putReq);
        return buildResponse(200, "{\"status\":\"accepted\"}");
    }
}

ML Pipeline: From Raw Events to Predictions

The ML pipeline transforms raw JSONL events into predictions through automated feature engineering, XGBoost training, and real-time inference endpoints.

Critical Lessons Learned: 5 Bugs You Must Avoid

Performance & Cost Analysis

Conclusion