What you'll learn
- Support responsible and trustworthy AI efforts, including privacy, transparency and bias checks
- Identify business needs and evaluate AI solution feasibility
- Identify data needs and evaluate data readiness for AI initiatives
- Manage AI/ML model development and evaluation
- Operationalise AI solutions and manage their lifecycle
Course outline
Domain I: Support Responsible and Trustworthy AI Efforts
- Oversee privacy and security plan
- Manage AI/ML transparency
- Conduct bias checks
- Monitor regulatory and policy compliance
- Manage accountability documentation and audit trail
Domain II: Identify Business Needs and Solutions
- Identify the problem to be solved
- Evaluate initial AI feasibility
- Conduct risk assessments
- Develop the AI project scope statement
- Define success criteria and support business case creation
Domain III: Identify Data Needs
- Define required data and identify data SMEs
- Identify data sources and locations
- Gather required data and check privacy, compliance and access
- Evaluate data quality and determine if data meets solution needs
Domain IV: Manage AI Model Development and Evaluation
- Oversee AI/ML model technique selection
- Oversee AI/ML model QA/QC
- Manage AI/ML model training
- Manage data transformation for data preparation
- Verify readiness for operationalization
Domain V: Operationalize AI Solution
- Manage the AI solution deployment plan and deployment
- Oversee model governance
- Oversee AI solution metrics
- Prepare final report and lessons learned
- Manage the AI solution transition and contingency plans