Data & machine learning

A real-time machine learning pipeline running 24/7 on AWS

A production system that ingests live data over WebSocket, runs a four-model ensemble and delivers predictions to Telegram with no manual steps.

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Client: Internal product; Remote · 2025
Model ensemble: 0; Model ensemble
Self-healing data ingestion: 0/7; Self-healing data ingestion
Infrastructure cost on free tiers: $0; Infrastructure cost on free tiers
Manual steps after deployment: 0; Manual steps after deployment

The challenge

Where they were stuck

Manual data analysis was slow, inconsistent and needed constant human attention. There was no system that automated the pipeline from raw data collection through to actionable signals reaching the people who needed them.

What we did

The solution we built

We built a self-healing WebSocket scraper running around the clock on AWS EC2, feeding a four-model ensemble of XGBoost, LightGBM, Random Forest and a combiner. Online learning with River lets the models improve as new data arrives.

Signals are delivered automatically to Telegram, and a live dashboard on Vercel shows system health and prediction history. The whole stack runs on free-tier infrastructure: AWS EC2, MongoDB Atlas, Vercel and Next.js.

The outcome

What changed

A fully automated end-to-end prediction system running live on AWS with no manual work after deployment.
Real-time data in, ensemble predictions out, and results in Telegram within seconds.
A reference architecture we now reuse for client data pipelines and monitoring systems.

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