Introducing etsi.ai - Practical and Open-Source Tools

Priyansh Srivastava — Tue, 06 Jan 2026 13:53:59 +0000

We’re a small open-source group building useful AI tools - the kind you actually want in real ML pipelines. No bloated frameworks. No “soon” features. Just practical stuff, shipped.

Right now, we’ve released three libraries - each built around a real pain-point we’ve hit ourselves 👇

🐺 etsi-watchdog - “is my data drifting?”

Data drift is one of those problems that everyone knows exists but nobody monitors properly because most tools are either too heavy or too rigid.

etsi-watchdog fixes that.

Production-first monitoring
Static & rolling window drift detection
PSI / KS-Test / Wasserstein built-in
Isolation Forest support
Slack alerts
JSON + visual reports
Plug-in architecture so you can register your own drift logic

Lightweight. PyData-native. Drop-in friendly.

🔍 etsi-failprint - “why is my model failing?”

Accuracy tells you how often you fail.
Failprint tells you why.

It automatically finds failure patterns across:

Tabular data
NLP
Computer Vision

with:

auto-segmentation of weak spots
semantic clustering for text + images
meta-feature failure analysis
counterfactual suggestions
clean Markdown reports

And it’s lazy-loaded, so you only pay for the dependencies you actually use.

🌋 etsi.etna - a tiny neural-net library for structured data

Most ML tools force a choice:

simple or fast.

Etna says: why not both?

Rust-powered core
Python-first API
auto task detection (classification / regression)
auto preprocessing
built-in metrics
MLflow tracking by default

Perfect for quick experiments, teaching, and small-scale production.

🧠 Why we’re building this

We just want AI tooling that feels:

simple
transparent
fast
well-designed
actually usable in production

So we’re making it — in the open.

⭐ If this sounds like your vibe

Check out the libs, try them, break them, open issues, contribute, hang out.

PyPI

We’re building. And we’re having fun doing it.

More soon. 🚀