MLOps operationalises machine learning, taking models reliably to production and keeping them performing. This is the ML Model Lifecycle and MLOps from the Stratenity Library, a governed consulting deliverable that is already built for you. It gives your team the full working method for ML model lifecycle and mlops, and it walks through everything from data and feature pipelines and feature store to deployment patterns (batch, online, edge). Every asset ships with a worked client example, a clear RACI, defined KPIs, and governance you can defend, so your team adapts it to a live client engagement instead of starting from a blank page.
What this asset covers
This is a preview of the full deliverable. Inside the Stratenity Library, this asset walks a consultant through the complete method, section by section, with a worked client example threaded throughout.
- MLOps Ambition and Maturity Target
- Reference MLOps Architecture
- Data and Feature Pipelines and Feature Store
- Experimentation and Model Development
- CI/CD/CT for ML
- Model Registry and Versioning
- Deployment Patterns (Batch, Online, Edge)
- Monitoring, Drift and Observability
- Automated Retraining and Rollback
- Platform, Tooling and Environments
- Team Topology and Roles
- Governance and Reproducibility
- Risks, Assumptions, Issues and Dependencies (RAID)
Review, download, and activate in 48 hours
Stratenity Library subscribers can review this asset in full, download it, and activate it into a governed, client-ready deliverable in 48 hours rather than weeks. Each asset ships with a worked client example, a clear RACI, defined KPIs, and governance you can defend.
Read the full asset on the Stratenity Library › Not a member yet? Start a free trial to unlock and activate it, or sign in if you already have an account.