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YAAI Monitoring

YAAI Monitoring

Yet Another AI Monitoring -- because the existing ones didn't fit and building your own seemed like a good idea at the time.

Most ML monitoring tools make you configure dashboards by hand, wire up custom pipelines, and learn their specific way of thinking before you see any value. YAAI takes a different approach: tell it what your model's inputs and outputs look like, send data, and everything else -- dashboards, drift detection, scheduled checks, alerting -- happens automatically.

Models Overview

The idea in 30 seconds

1. Define your model schema     "age is numerical, region is categorical, ..."
2. Send inference data           POST /api/v1/inferences  { inputs, outputs }
3. Dashboards appear             per-feature histograms and bar charts, auto-generated
4. Drift detection runs          scheduled jobs compare distributions over time
5. Alerts fire when things shift "PSI for 'age' jumped to 0.32 -- threshold is 0.2"

No pipeline integration. No YAML property mappings. No dashboard builder. The schema is the config.

Tip

New here? Start with the Getting Started guide -- it takes you from zero to dashboards in five minutes.

Documentation

- **[Getting Started](getting-started.md)** -- from zero to dashboards in five minutes - **[Server Setup](server-setup.md)** -- local development with PostgreSQL, env vars, authentication - **[Deployment](deployment.md)** -- Docker Compose, pip install, Google Cloud SQL - **[Core Concepts](concepts.md)** -- models, versions, schemas, drift -- how the pieces fit - **[Drift Detection Guide](drift-guide.md)** -- PSI, KS test, Chi-squared, JSD explained with visuals - **[Architecture](architecture.md)** -- system design, tech stack, how the backend is structured - **[REST API Reference](reference/api.md)** -- full OpenAPI spec, auto-generated from the server - **[Python SDK Reference](reference/sdk.md)** -- async client docs, auto-generated from source

When to use YAAI

  • You want monitoring running in minutes, not days
  • You prefer REST APIs over SDK-heavy integrations
  • You don't want to build dashboards manually
  • You need drift detection without becoming a statistician
  • You value simplicity over feature completeness

When NOT to use YAAI

  • You need deep ML pipeline integration -- look at Evidently
  • You want custom drift algorithms beyond the standard four
  • You need multi-tenant SaaS deployment
  • You need battle-tested production stability -- this is young software