Machine Learning Lifecycle Framework
Deliver and operate ML models reliably from problem to production.
Methodology
Lifecycle model across problem framing, data prep, training, validation, deployment, monitoring and retraining (MLOps).
Components
Problem framing; Data preparation; Training & validation; Deployment; Monitoring & drift; Retraining & retirement.
Governance
Data science owns delivery; MLOps operates; model risk validates; owners accept. L1 Initial L2 Developing L3 Defined L4 Managed L5 Optimised Ad hoc Basic, siloed Standardised & Quantified & integrated Predictive & embedded governed
Maturity levels
Implementation roadmap
Diagnose (assess maturity) → Design (tailor framework & governance) → Build (policies, standards, controls, pipelines) → Embed (training, culture, adoption) → Assure (test, benchmark, re-score).
Deliverables
Framework document, governance & RACI, policy/standard templates, maturity score & roadmap, board reporting pack.
Advisory opportunities
MLOps build; Model lifecycle review; Monitoring & drift programme.
Across the Data & AI ecosystem
Knowledge graph · 4 relations