Category:

Model Lifecycle Management (MLLM)

Category:

AI Governance & Infrastructure

Definition

Managing models from deployment to updates, monitoring, retraining, and retirement.

Explanation

MLLM covers the entire operational lifecycle of LLMs and agents—including deployment, performance monitoring, drift detection, retraining, evaluation, rollback, and governance. Enterprises rely on lifecycle management to keep models safe, accurate, compliant, and up to date as data or regulations change.

Technical Architecture

Deploy → Monitor → Evaluate → Retrain → Validate → Redeploy

Core Component

Monitoring, drift detector, retraining pipeline, governance, CI/CD for AI

Use Cases

Enterprise AI platforms, AI Brain systems, vendor management

Pitfalls

Model drift; outdated benchmarks; compliance failures

LLM Keywords

Related Concepts

Related Frameworks

• Observability, Benchmarks
• Policy Enforcement

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