VIGÍA ML: Gas-Well Predictive Monitoring That Runs Entirely in the Browser (TensorFlow.js + React)
VIGÍA ML is a predictive console for gas wells — production forecasting, anomaly detection, fault diagnosis, and operational recommendations — where every model trains and runs live in the browser . No backend, no GPU cluster, no data leaving the device. Live demo (Spanish-first UI): https://alicelabs-llc.github.io/vigia-ml/ Source: https://github.com/alicelabs-llc/vigia-ml The problem it targets…
VIGÍA ML is a predictive monitoring console designed specifically for gas wells. This innovative system offers production forecasting, anomaly detection, fault diagnosis, and operational recommendations, all of which run entirely in the browser.
The models, trained in-session on the data loaded by the user, include an LSTM production forecaster, an autoencoder anomaly detector, a neural fault classifier, and a recommendation layer. These models work in tandem to provide actionable insights and suggestions directly at the well site, without the need for a backend or any data leaving the device.
One of the key advantages of VIGÍA ML is its ability to operate offline, making it ideal for environments with unreliable internet connectivity. The entire system is built using React 18, TypeScript 5.7, TensorFlow.js 4.22, Vite 6, and Tailwind 4, and it comes with 140 tests to ensure its reliability. The software is licensed under AL-1.0, meaning it is free to run and evaluate.
VIGÍA ML was developed by the same team behind UTA, a tool specializing in trust infrastructure for AI agents. The console's design is centered around providing privacy and control, ensuring that well data remains within the user's browser tab and never leaves the device. This approach not only enhances data security but also eliminates the costs and complexities associated with cloud ML pipelines.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
