RKB109/production-ai-observability-20260909-model

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: failureclassaccuracy, alertprecision, tracecoverage

Intended Use

• —Architecture prototyping
• —CI and evaluation examples
• —Local baseline comparisons
• —Educational experimentation

Hugging Face Task Coverage

• —text-classification
• —token-classification
• —summarization
• —zero-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.