The latest articles on DEV Community by Syed Meesam Turab (@meesambukhari). https://dev.to/meesambukhari https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F1110317%2Fe2976f44-7bb7-4571-9e73-729f6b556d55.jpeg https://dev.to/meesambukhari en Syed Meesam Turab Sun, 07 Jun 2026 20:12:37 +0000 https://dev.to/meesambukhari/building-an-ml-powered-notification-routing-engine-on-aws-14a1 https://dev.to/meesambukhari/building-an-ml-powered-notification-routing-engine-on-aws-14a1 <p>Most applications send notifications using fixed schedules or generic delivery rules.</p> <p>The challenge is that users have different engagement patterns. A notification sent at the wrong time is more likely to be ignored, contributing to notification fatigue and lower engagement.</p> <p>Recently, I came across an interesting project by Yadab Sutradhar called <strong>Smart Notification Routing Engine</strong>, which tackles this problem using machine learning to predict the optimal notification delivery time for each user.</p> <p>GitHub Repository:<br> <a href="https://github.com/Yadab-Sd/smart-notification-routing-engine" rel="noopener noreferrer">https://github.com/Yadab-Sd/smart-notification-routing-engine</a></p> <h2> Key Features </h2> <ul> <li>ML-driven send-time optimization</li> <li>Real-time prediction using Amazon SageMaker</li> <li>Event-driven architecture</li> <li>Multi-channel notification support</li> <li>AWS serverless infrastructure</li> <li>Automated model training pipelines</li> </ul> <h2> Architecture </h2> <p>The project leverages:</p> <ul> <li>AWS Lambda</li> <li>Amazon SageMaker</li> <li>Kinesis Data Streams</li> <li>EventBridge Scheduler</li> <li>DynamoDB</li> <li>Apache Spark</li> <li>AWS CDK</li> </ul> <h2> How It Works </h2> <ol> <li>User interaction events are collected through the ingestion layer.</li> <li>Spark ETL jobs transform raw event data into machine learning features.</li> <li>SageMaker trains and deploys prediction models.</li> <li>A decision service predicts the best notification delivery time.</li> <li>EventBridge automatically schedules notifications for delivery.</li> </ol> <h2> Why It's Interesting </h2> <p>Instead of relying on static notification schedules, this system aims to personalize delivery timing based on user behavior, helping improve engagement while reducing unnecessary notification overload.</p> <p>If you're interested in MLOps, event-driven systems, AWS serverless architectures, or recommendation and personalization systems, this project is worth exploring.</p> <p>Repository:<br> <a href="https://github.com/Yadab-Sd/smart-notification-routing-engine" rel="noopener noreferrer">https://github.com/Yadab-Sd/smart-notification-routing-engine</a></p> <p>Would love to hear what others think about this approach to intelligent notification delivery.</p> aws serverless java machinelearning