• Definition of AI Concepts
• Machine Learning (What, Types, How)
• AI Application Domains
• Benefits & Risks of AI
• Maple Stream Digital’s Framework
– Introduce concepts & tools to aid
process execution.
• Real-world case study walks through

• Build/Own a business goal for AI.
• Identify users, stakeholders, and their expectations.

• Define User Needs
• Intersection of User Needs with AI Capabilities
• As-IS workflows vs. To-be
• AI Value Map (Intersection of user needs vs. AI capabilities)
• Evaluate AI
– AI Intent vs. Data Needs
– Anticipate AI outcomes and their
effects.
– Define Objective Function for AI
• User Stories and Definition of Done for AI

• Define Prototype or MVP
• Define success criteria.
• Define testing plan.
• Prototype to Production

• Data Collection & Evaluation
– Data sourcing & validation
– Design raters & labeling
• Involvement with Model Training and Fine- tuning
– Deployment strategies
– Model Evaluation strategies
– How to plan for Continuous maintenance & mgmt.

• User Expectation for adoption
• User Onboarding strategies
• Design for experiments/User Analytics
• Plan for co-learning
• Error controls
• Adapt for user needs.
• Calibrate trust.
• Sub-topic: Optimization
• Feedback & Control
• Design for model tuning/retraining
• Balance control & automation
• Errors & graceful failure

• Group presentation of capstone projects
• Review Course outcomes & benefits