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.
- Client
- Internal product
- Remote · 2025
- Model ensemble
- 4
- Self-healing data ingestion
- 24/7
- Infrastructure cost on free tiers
- $0
- Manual steps after deployment
- 0
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.
Ready when you are
Want a result like this?
Tell us where you are stuck. We will tell you what it would take, what it would cost and when it could ship.
- Projects shipped since 2025
- 16+
- Reply to every enquiry
- < 12h
- Code and accounts in your name
- 100%
- Post-launch fixes included
- 30 days
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