Production AI Observability Monitor Baseline Model
Model Description
This repository contains a small, transparent prototype model for
Production AI teams need trace-level signals for latency, token growth, tool failures, and low-quality outputs.
The model combines per-label token weights with IDF-weighted evidence
retrieval. It was generated for reproducible architecture demonstrations and
does not call a hosted LLM.
Evaluation
- Held-out synthetic examples: 4
- Accuracy: 1
- Intended metrics: failure_class_accuracy, alert_precision, trace_coverage
Intended Use
- Architecture prototyping
- CI and evaluation examples
- Local baseline comparisons
- Educational experimentation
Hugging Face Task Coverage
text-classificationtoken-classificationsummarizationzero-shot-classification
Limitations and Risks
Thresholds are demonstration defaults and need calibration against each production workload.
The dataset is synthetic and small. Do not use this model for consequential
decisions without representative data, expert review, and production-grade
evaluation.
Reproducibility
The linked GitHub repository includes train.py, the exact dataset split,
evaluation code, and the model JSON format.
