Building an ML-Powered Notification Routing Engine on AWS

Source: Dev.to

Most applications send notifications using static schedules. The problem is that users have different engagement patterns. A notification sent at the wrong time often gets ignored, leading to notification fatigue and reduced engagement. To address this challenge, I built a Smart Notification Routing Engine that uses machine learning to predict the optimal delivery time for each user. GitHub Repository: https://github.com/Yadab-Sd/smart-notification-routing-engine Key Features • ML-driven send-time optimization Architecture The platform uses: • AWS Lambda Workflow: User events are collected through the ingestion layer. Spark ETL jobs transform raw data into ML features. SageMaker trains and deploys prediction models. The decision service predicts the optimal delivery time. EventBridge schedules notifications automatically. Project Goal The primary objective is reducing notification fatigue while improving engagement through personalized delivery timing. If you’d like to review the architecture, codebase, or share feedback, feel free to check out the repository: https://github.com/Yadab-Sd/smart-notification-routing-engine Feedback, suggestions, and contributions are welcome.