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 strategic execution asset 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 your team 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