DEV Community: Yadab Sutradhar

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

Yadab Sutradhar — Wed, 08 Apr 2026 06:20:56 +0000

Author Bio: ML Engineer specializing in real-time ML systems and serverless architectures on AWS. Built production notification routing systems, processing millions of events daily.

Introduction
The Problem
Solution Architecture
Core Components
ML Pipeline
Implementation
Lessons Learned
Performance
What’s Next
Conclusion

Introduction

Sending notifications at the wrong time is like knocking on someone’s door at 3 AM.

Even if the message is important — 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.

What We’ll Build

Real-time ML inference system using Amazon SageMaker
Event-driven architecture processing millions of events
Automated ML training pipeline with feedback loops
Infrastructure as Code using AWS CDK
Cost-optimized serverless system

Tech Stack

AWS Lambda (Java 21 + SnapStart)
Amazon SageMaker (XGBoost)
AWS Glue (PySpark ETL)
Amazon Kinesis Data Streams
DynamoDB
EventBridge Scheduler
AWS CDK (TypeScript)

...

The Problem: Notification Fatigue

Business Challenge

Modern applications send billions of notifications daily.

But:

50–70% of notifications go unread
User churn increases by 30% due to notification fatigue
Engagement rates vary 5–10x depending on timing

Traditional Approaches

“Send at 9 AM local time”
“Send when user was last active”
“Batch and send at fixed intervals”

These approaches fail to account for individual user behavior.

Technical Challenges

Real-time prediction (<500ms latency)
Personalized decision per user
Scale to millions of events
Continuous learning via feedback
Cost optimization

...

Solution Architecture

System Flows

Event Ingestion (real-time)
Decision & Scheduling (real-time)
ML Training Pipeline (batch, daily)

...

Design Principles

Event-Driven & Decoupled

Kinesis for streaming
Services communicate via events
Independent scaling

Serverless-First

Lambda for compute
DynamoDB for storage
No infrastructure management

ML Feedback Loop

Delivery outcomes feed training
Daily retraining
Continuous improvement

...

Core Components

Event Ingestion (Control Plane)

Accepts user events (clicks, sends, etc.)
Streams data to Kinesis

Key Decisions:

Partition by userId → ordered processing
Async processing → low latency
SnapStart → reduced cold start

Event Processing

Stores raw data in S3 (data lake)
Updates DynamoDB user profiles

Key Decisions:

Dual writes (S3 + DynamoDB)
Atomic counters
Partitioned storage

Decision Service (Real-Time ML)

Predicts optimal send time
Calls SageMaker endpoint

Flow:

Fetch user profile
Evaluate each hour in time window
Score each candidate time
Select best hour
Schedule notification

...

ML Pipeline

End-to-End Flow

S3 Raw → Glue ETL → Features → SageMaker Training → Model → Endpoint ↑____________________________________________________________↓ ………………………..Feedback Loop (Clicks, Opens)…………………….

Feature Engineering

Hour of day
Click rate (last 7 days)
Number of sends per hour

Model Choice: XGBoost

Fast inference (<50ms)
Excellent for tabular data
Built-in SageMaker support
Handles overfitting well

Orchestration

EventBridge → daily trigger
Step Functions → workflow
Glue → feature generation
SageMaker → training

...

Implementation Highlights

DynamoDB (Single Table Design)

{
  "pk": "USER#user_12345",
  "sk": "PROFILE",
  "lastSeenAt": "...",
  "counters": {
    "events": 1523,
    "notifications_sent": 47,
    "notifications_clicked": 12
  }
}

Benefits:

Single read operation
Atomic updates
Cost-efficient

Infrastructure as Code (CDK)

Version-controlled infrastructure
Reproducible deployments
Type-safe configurations

...

Lessons Learned

Feature Mismatch

Training features ≠ inference features → model breaks

👉 Always keep features consistent

Data Format Issues

CSV vs Parquet mismatch → failed training

👉 Standardize data formats

Wrong Event Types

Incorrect filters → zero training data

👉 Validate data pipelines

[NOTE: Actively updating this part]

SageMaker Mode Confusion

Mixing built-in + custom training → broken setup

👉 Choose one approach

Time Window Bugs

Using all-time data instead of last 7 days

👉 Always explicitly filter time windows

...

Performance & Cost

Performance

API latency (p50): 180ms
Inference latency: 45ms
Throughput: 5,000 events/sec

Cost

Under Estimation(1M events/day)

...

Optimizations

Lambda SnapStart
DynamoDB on-demand
S3 intelligent tiering
Small SageMaker instances

...

What’s Next

Multi-Armed Bandits (better optimization)
Multi-channel prediction (email vs SMS vs push)
A/B testing framework
Feature Store integration
Model monitoring & drift detection
AutoML integration

Conclusion

Building ML systems is:

10% modeling and 90% engineering

The real challenges are:

Data pipelines
Feature consistency
Scalability
Feedback loops
Production debugging

Key Takeaways

Use event-driven architecture
Keep training and inference features aligned
Prefer managed ML services
Use Infrastructure as Code
Optimize costs early
Always implement feedback loops

Discussion

Have you built similar systems?

What challenges did you face?
How do you ensure feature consistency?
Let’s discuss 👇

Yadab-Sd / smart-notification-routing-engine

Smart Notification Routing Engine

ML-Powered Intelligent Notification Delivery System with Real-Time Optimization

Project Status: 🚧 Active Development - Core infrastructure and ML pipeline implemented. Performance benchmarking and production validation in progress.

Overview

A production-grade, enterprise-scale notification routing engine designed to leverage machine learning for optimizing message delivery timing and channel selection. This system addresses the critical problem of notification fatigue by intelligently predicting when users are most likely to engage with notifications, with projected engagement rate improvements of 40-60% compared to traditional uniform delivery strategies.

Built entirely on AWS serverless architecture, the system processes millions of events, trains ML models nightly, and serves real-time predictions with sub-second latency—all while maintaining strict security, observability, and cost optimization standards.

Key Capabilities

🤖 ML-Driven Send-Time Optimization: XGBoost models predict optimal delivery windows per user
📊 Real-Time Feature Engineering: Apache Spark ETL pipelines transform raw events into ML features
⚡ Sub-Second…

View on GitHub

Tags: #AWS #MachineLearning #SageMaker #Serverless #MLOps #XGBoost #EventDriven #CDK

Email Sender Service

Yadab Sutradhar — Sun, 26 Jan 2025 02:52:43 +0000

NPM GitHub

📧 Description

A simple Node.js application that sends emails using the nodemailer package. This service ensures smooth email delivery with minimal setup.

🚀 Features

Lightweight and customizable codebase.
Designed to be beginner-friendly.
Easy to configure.
Helpful for job seekers to send emails to hiring managers and recruiters with bulk/batch for free!

🛠️ Prerequisites

Before starting, make sure you have the following installed on your system:

Node.js (v18 or higher)
npm or yarn

Usage

Step 1 - Installation

npm install job-email-sender

yarn add job-email-sender

Step 2 - Configuration

Add this code to your program file-

const emailConfig = {
  service: 'your-email-service', // Example- `gmail`, `yahoo` etc.
  user: 'your-email@example.com', // your email address (sender/from)
  pass: 'your-email-password', // your app password for your email account
  name: 'your-display-name'
};

Notes:

Replace placeholders like your-email@example.com, your-display-name, with your actual details.
Use gmail, yahoo etc. in service placeholder. Whereever is your actual email account in.
For pass, this is not your email login password. It is a app password which can be collected from your email account. For details follow-

Enable App Passwords:

Yahoo : Follow instructions to get app password for yahoo mail.

Gmail : Follow Google App Password to get app password.

Other Services : Follow their own instructions to get app password.

Step 3 - Apply

For JavaScript Users:

const { EmailSender } = require('job-email-sender')

For TypeScript Users:

import { EmailSender } from 'job-email-sender'

Construct & send

const emailSender = new EmailSender(emailConfig)

const contacts = [{ email: 'receiver-name@example.com', name: 'Don' }]
const message = 'Hello ${name}, this is your email content!' // you can add html here. Example - 'Hello ${name}, <p><b>this</b> is a test email!</p>'
const subject = 'This is Your Email subject'

emailSender
  .sendEmails(contacts, message, subject)
  .then((message) => {
    console.log(message)
  })
  .catch((error) => {
    console.error(error)
  })

[!Note] Remember !
This package is primarily for backend usage only. Because it is node.js based and that doesn't run on browser.
If you want to use this on frontend (browser), you need to run your frontend on server (server-side rendering) using express.

[!Note]
Managing attachments - Coming soon...

DEV Community: Yadab Sutradhar

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

Table of Contents

Introduction

What We’ll Build

Tech Stack

The Problem: Notification Fatigue

Business Challenge

Traditional Approaches

Technical Challenges

Solution Architecture

System Flows

Design Principles

Event-Driven & Decoupled

Serverless-First

ML Feedback Loop

Core Components

Event Ingestion (Control Plane)

Key Decisions:

Event Processing

Key Decisions:

Decision Service (Real-Time ML)

Flow:

ML Pipeline

End-to-End Flow

Feature Engineering

Model Choice: XGBoost

Orchestration

Implementation Highlights

DynamoDB (Single Table Design)

Infrastructure as Code (CDK)

Lessons Learned

Feature Mismatch

Data Format Issues

Wrong Event Types

SageMaker Mode Confusion

Time Window Bugs

Performance & Cost

Performance

Cost

Optimizations

What’s Next

Conclusion

Key Takeaways

Discussion

Yadab-Sd / smart-notification-routing-engine

Smart Notification Routing Engine

Overview

Key Capabilities

Email Sender Service

📧 Description

🚀 Features

🛠️ Prerequisites

Usage

Step 1 - Installation

Step 2 - Configuration

Notes:

Enable App Passwords:

Step 3 - Apply