Arize Observability

DevOps & Security

arize-observability

Arize Observability is an Agent Skill for devops & security published in the a5c-ai/babysitter repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Arize AI skill for production ML monitoring, embedding drift, and performance analysis.

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How to use this skill

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Prompt to paste

Inspect the Agent Skill at https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/data-science-ml/skills/arize-observability/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/arize-observability, but wait for my approval before writing files or running scripts.

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arize-observability

Overview

Arize AI skill for production ML monitoring, embedding drift detection, and comprehensive performance analysis.

Capabilities

Production data logging
Embedding drift detection for NLP/CV models
Performance monitoring dashboards
Root cause analysis
Slice and dice analysis for segments
Bias monitoring
A/B test monitoring
Custom metrics and monitors

Target Processes

Model Performance Monitoring and Drift Detection
ML System Observability and Incident Response
Model Evaluation and Validation Framework

Tools and Libraries

Arize AI SDK
pandas
numpy

Input Schema

{
  "type": "object",
  "required": ["action"],
  "properties": {
    "action": {
      "type": "string",
      "enum": ["log", "monitor", "analyze", "alert-config", "compare"],
      "description": "Arize action to perform"
    },
    "logConfig": {
      "type": "object",
      "properties": {
        "modelId": { "type": "string" },
        "modelVersion": { "type": "string" },
        "modelType": { "type": "string", "enum": ["score_categorical", "regression", "ranking"] },
        "environment": { "type": "string", "enum": ["training", "validation", "production"] },
        "dataPath": { "type": "string" },
        "predictionIdColumn": { "type": "string" },
        "timestampColumn": { "type": "string" },
        "featureColumns": { "type": "array", "items": { "type": "string" } },
        "embeddingColumns": { "type": "array", "items": { "type": "string" } },
        "predictionColumn": { "type": "string" },
        "actualColumn": { "type": "string" }
      }
    },
    "monitorConfig": {
      "type": "object",
      "properties": {
        "metrics": { "type": "array", "items": { "type": "string" } },
        "thresholds": { "type": "object" },
        "schedule": { "type": "string" }
      }
    },
    "analysisConfig": {
      "type": "object",
      "properties": {
        "analysisType": { "type": "string", "enum": ["drift", "performance", "fairness", "data_quality"] },
        "timeRange": { "type": "object" },
        "segments": { "type": "array", "items": { "type": "string" } }
      }
    }
  }
}

Output Schema

{
  "type": "object",
  "required": ["status", "action"],
  "properties": {
    "status": {
      "type": "string",
      "enum": ["success", "error"]
    },
    "action": {
      "type": "string"
    },
    "logId": {
      "type": "string"
    },
    "dashboardUrl": {
      "type": "string"
    },
    "analysis": {
      "type": "object",
      "properties": {
        "overallScore": { "type": "number" },
        "driftMetrics": { "type": "object" },
        "performanceMetrics": { "type": "object" },
        "topIssues": { "type": "array" },
        "recommendations": { "type": "array", "items": { "type": "string" } }
      }
    },
    "alerts": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": { "type": "string" },
          "severity": { "type": "string" },
          "triggered": { "type": "boolean" }
        }
      }
    }
  }
}

Usage Example

{
  kind: 'skill',
  title: 'Log production predictions to Arize',
  skill: {
    name: 'arize-observability',
    context: {
      action: 'log',
      logConfig: {
        modelId: 'fraud-detector',
        modelVersion: '2.0.0',
        modelType: 'score_categorical',
        environment: 'production',
        dataPath: 'data/production_predictions.parquet',
        predictionIdColumn: 'request_id',
        timestampColumn: 'timestamp',
        featureColumns: ['amount', 'merchant_category', 'hour'],
        predictionColumn: 'fraud_probability',
        actualColumn: 'is_fraud'
      }
    }
  }
}