II.
Responsibility overview
Reference · liveresponsibility:model-quality-assurance
Model quality assurance overview
Validate ML model quality through eval harnesses, drift detection, performance benchmarks, and fairness audits before and after deployment.
Attributes
displayName
Model quality assurance
cadence
continuous
description
Validate ML model quality through eval harnesses, drift detection,
performance benchmarks, and fairness audits before and after deployment.
Outgoing edges
held_by3
- role:ml-engineer·RoleMachine Learning Engineer
- role:prompt-engineer·RolePrompt Engineer
- role:ai-ethics-researcher·RoleAI Ethics Researcher
requires_expertise2
- skill-area:eval-driven-development·SkillAreaEval-Driven LLM Development
- skill-area:model-evaluation·SkillAreaModel Evaluation & Selection
Incoming edges
holds_responsibility8
- role:applied-scientist·RoleApplied Scientist
- role:nlp-engineer·RoleNLP Engineer
- role:computer-vision-engineer·RoleComputer Vision Engineer
- role:speech-engineer·RoleSpeech Engineer
- role:reinforcement-learning-engineer·RoleReinforcement Learning Engineer
- role:prompt-engineer·RolePrompt Engineer
- role:ml-infrastructure-engineer·RoleML Infrastructure Engineer
- role:ai-ethics-researcher·RoleAI Ethics Researcher
triggers_responsibility1
- workflow:model-training-pipeline·WorkflowModel Training Pipeline