Machine learning, and other types of artificial intelligence, imitate human thought in order to improve decision making. But when you create a model that thinks for itself, you risk unleashing a dragon. You might make biased decisions, mishandle sensitive data, upset customers, or create compliance problems.

Can enterprises tame the dragon? Maybe. They need to govern the full AI/ML lifecycle—data and feature engineering, model development, and model operations—with special attention to the operations stage. They need to carefully monitor, control, and reuse models during the entire lifecycle.

Eckerson Group analysts study this problem. Our consultants live it. Join both for a free-wheeling discussion of the tradeoffs and pitfalls of AI/ML governance.

You will hear:

• What AI/ML governance means, and why enterprises need it
• How governance applies to key stages of the AI/ML lifecycle
• Guiding principles, best practices, and tradeoffs of AI/ML governance
• Case studies and what they teach us
• How AI/ML governance is likely to evolve

Speakers: 

- Wayne Eckerson, President at Eckerson Group

- Kevin Petrie, VP of Research at Eckerson Group

- Joe Hilleary, Research Analyst at Eckerson Group

- Nick Elprin, CEO & Co-Founder at Domino Data Lab

- Stephen Smith, CEO at JogNog

- Kurt Thearling, Executive Leader at Thearling Analytic Advisors

- Carlos Bossy, Consultant at Datalere

- Sanjeev Mohan, Senior Advisor at Eckerson Group