drift-detection

id	SKL-drift-DRIFTDETECTION
name	Drift Detection
description	This is an **alias skill** so docs can reference `77-mlops-data-engineering/drift-detection`. In this repo, drift guidance is covered by: - `77-mlops-data-engineering/drift-detection-retraining` (trig
version	1.0.0
status	active
owner	@cerebra-team
last_updated	2026-02-22
category	Backend
tags	["api","backend","server","database"]
stack	["Python","Node.js","REST API","GraphQL"]
difficulty	Intermediate

Drift Detection

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

This is an alias skill so docs can reference 77-mlops-data-engineering/drift-detection. In this repo, drift guidance is covered by:

• 77-mlops-data-engineering/drift-detection-retraining (triggering retraining)
• 06-ai-ml-production/ai-observability (monitoring + alerts)

Why This Matters

• :
• :
• :

Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • <e.g., env vars, request payload, file paths, schema>
• Entry Conditions:
  • <Pre-requisites: e.g., Repo initialized, DB running, specific branch checked out>
• Outputs:
  • <e.g., artifacts (PR diff, docs, tests, dashboard JSON)>
• Artifacts Required (Deliverables):
  • <e.g., Code Diff, Unit Tests, Migration Script, API Docs>
• Acceptance Evidence:
  • <e.g., Test Report (screenshot/log), Benchmark Result, Security Scan Report>
• Success Criteria:
  • <e.g., p95 < 300ms, coverage ≥ 80%>

Skill Composition