AI Evaluation Pipeline - Managers

Earning the Right to Production: The Data Manager's AI Evaluation Masterclass

Data & AI Managers: are your AI evaluations strictly testing technical metrics like accuracy and latency while ignoring business accountability?

The current industry trap is that most enterprise evaluations cannot return a 'No', which turns AI governance into a mere formality.
Join us this Thursday as we kick off our comprehensive webinar series exploring "The Cradle-to-Grave AI Evaluation Pipeline". While our broader series will tackle this pipeline from the perspectives of the Data & AI Citizen, Professional, and Executive, this Thursday’s session is designed exclusively for the Data & AI Manager.
We will bridge the gap between strategy and governed execution, moving beyond Prioritisation (deciding which AI use cases to build) to rigorous Evaluation (determining whether a model has earned the right to enter or remain in production).
Here is what we will cover across the lifecycle phases to put you back in control:

• Inception & Scoping (Upper Spine): Learn how to translate Outcome & KPI baselines, Decision Nodes, and Asymmetric Error Cost Matrices into hard evaluation criteria. You will be equipped to make the "Kill or Pivot" call to stop ungrounded hype that lacks measurable business rules.
• Data Supply & Semantics (Lower Spine): We will unpack how Semantic Models, Data Product Canvas Contracts, and Critical Data Elements (CDEs) populate your evaluations. You will learn exactly when to make the "Halt the Build" decision to block pipeline training if the data supply lacks proper contracts or semantic bindings.
• Verification & Release (Pre-Release Gates): Discover how to synthesize the 4 Quality Pillars (Business, Decision, Data, Records) and execute Recomputation Discrepancy Audits. This enables the "Reject or Pilot" decision, helping you stand firm against delivery pressure when manual passes mask critical mathematical failures.
• In-Flight Run & Retirement (Production & Sunset): Master Continuous Value Drift Monitoring, track Covariate Shift, and maintain Defensible Decision Records. You will learn how to set Pre-defined Decommission Rules so you know exactly when to "Pull the Plug" on a live model.

Empower yourself to make the difficult operational calls based on mathematical and operational truth, rather than algorithmic opacity.
Are you ready to transition your AI governance from a rubber stamp to a defensible framework?