ML Model Lifecycle & MLOps

MLOps operationalises machine learning — taking models reliably to production and keeping them performing. This template structures the ML lifecycle across experimental, production, and monitoring phases, applying DevOps principles (CI/CD, automation, version control) plus model monitoring, retraining, and governance.

Deliverable CodeAT-STR_STR-AI-009

Versionv1.0

CategoryAI & Data Frameworks

PublishedJuly 14, 2026

Reading time21 min

Prepared byStratenity Advisory Team

IntelligenceValue chainIndustryCross-industryPracticeAI StrategyFocus04 Transformation Blueprints

AI Intelligence

Capability

Process AutomationAI Governance

Stage

Discover›Assess›Prioritize›Design›Govern›Deploy›Adopt›Measure›Scale

Value levers

RiskProductivity

ml model lifecycle and mlopsdata and feature pipelines / feature storeexperimentation and model developmentci/cd/ct for mlmodel registry and versioningdeployment patterns (batch, online, edge)mlops operationalisesoperationalises machine

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ML Model Lifecycle & MLOps

Stratenity is the AI Operating System for Strategic Execution™.

Stratenity is the AI Operating System for Strategic Execution^™.