Operationalizing Decision Intelligence

This unit teaches learners how to implement, govern, and scale decision intelligence in business contexts by connecting models, tools, metrics, and organizational processes. Students examine streaming and batch decision architectures, ModelOps practices, ethical risks (including bias), and how to measure and estimate the cost and value of deployed decisions.

Learning Objectives

• Analyze streaming and batch decision-making models and ModelOps workflows to determine appropriate deployment patterns for real-time and near-real-time business needs
• Apply tools, computing stack components, and automated visualization techniques to design and set up an end-to-end decision pipeline that produces the desired business action
• Evaluate models and decision processes for ethical risks, bias (e.g., discriminatory hiring algorithms), repeat offender failure modes, and other wrong influences, and design mitigation and governance strategies
• Estimate the cost, expected value, and key performance measures of decision intelligence initiatives and align metrics to business actions and downstream decision impacts