Operationalizing Decision Intelligence

This unit covers how to put decision intelligence into production: selecting between streaming and batch models, designing the computing and ModelOps stack, and aligning metrics, monitoring, and governance. Learners will measure decision value, estimate costs, detect and mitigate harmful influences (bias), and create plans to deploy, monitor, and iterate operational decision systems.

Learning Objectives

• Analyze trade-offs between streaming and batch decision-making architectures by quantifying decision value, latency, cost, and downstream business impact and recommend an appropriate architecture for a given scenario
• Apply ModelOps and deployment best practices to design a computing stack and pipeline that includes model serving, monitoring, automated visualizations, and tooling required for operational decision-making
• Demonstrate methods to detect, evaluate, and mitigate harmful influences and bias in deployed decision systems (for example, assessing a hiring model for discriminatory outcomes and implementing safeguards and corrective measures)
• Evaluate total cost of ownership and organizational impacts of deploying decision intelligence (including metrics alignment, executive roles, and changing business models) and produce a setup and monitoring plan for continuous iteration