Courses

Description:

This course will examine how the excitement around AI is matched only by the complexity of delivering it effectively. Moving from proof-of-concept to production means addressing the entire AI lifecycle, from data and model development to deployment, monitoring, and evolution. Organizations that succeed do so by treating AI delivery as a disciplined practice, not an ad-hoc initiative. Central to this is AI-driven automation, not just in model development, but across workflows, infrastructure, and governance. When done right, AI becomes a repeatable capability that scales with the business.

Objectives:

By the end of this course, you will be able to:

• Identify the right skills and experience needed for AI adoption

• Understand the required data quality and useability for successful AI programs

• Create essential development standards

• Identify the correct infrastructure and tooling for AI adoption

• Build robust governance and risk management processes

• Create stakeholder and end user engagement

• Develop plans for continuous improvement of the AI program

• Adopting a change management framework

• Develop a proactive and automated strategy for monitoring and maintenance

Modules:

This course contains the following modules:

• Introduction to Delivery Capability

• Building a Skilled, Multidisciplinary Team

• Establishing a Strong Data Foundation

• Creating AI Development Standards

• Infrastructure for Tools and Scalable Deployment

• Robust Governance and Risk Management

• Stakeholder Enablement and End User Engagement

• Continuous Improvement and Iteration

• Change Management Framework

• Monitoring and Maintenance

• Key Takeaways and Next Steps

Prerequisites:

Completion of the Getting Started with EOM course

Duration:

~ 30 minutes