North Dakota is well known for energy and food production, but less known is the states roots in technology. Rep. Josh Christy discusses North Dakota's ambitions in AI, what they mean, and how North Dakota is taking a holistic approach to safety and innovation.

Successful AI projects/ideas have many necessary ingredients, one of the most important being the people you identify and choose to be part of the project. How to you decide and how to you build culture? Ron Green is a successful entrepreneur, listen in as he discusses his most current company (almost seven successful years now!) as he walks us through finding the right folks to add to the team, how they build their culture of discovery and delivery at KungFu, and how they deliver projects to their clients.

The first Federal Chief AI Officer, Oki Mek, runs through three key components in building responsible and secure AI programs, something desperately needed in today's organizations.

How do you get all the cowfolk to giddy-up in the same direction? Smart people are notorious for having opinions; combine intelligence with the independent streak in North Dakota folks and it makes for a lot of work in driving change. Kim Weis has some insightful tips in lessons learned in trying change, failing, learning, and trying again.

Do you want some AI with that? It's everywhere, though thankfully not IN our coffee (yet). So if AI conversations are ubiquitous, how do we sort value from folly? Keatra Nesbitt is a practiced strategy leader and data scientist, listen in as she shares her thoughts on how she navigates these complex conversations with her clients.

Utilizing Gen AI towards your organization's specific needs presents both an opportunity and a challenge. Opportunity to take advantage of massive investments by large tech firms; challenges in that it can be difficult to know what is correct and usable at scale out of these projects. Sabre's Laura Palomino discusses novel approaches they've used towards that have helped her team, and others at Sabre, pursue innovation and change, and be more efficient in testing, and more proactive in resolving potential issues before users find them.

What does it mean to have AI ready data? And once I know that, what do I do about it? Ian Stahl is Director of Product Management @ Informatica and has seen many data centric applications come and go. He provides insights into what's happening in the market today and how we all may work better together to make data highly useable and fit for purpose.

We have come a long way since the publication of "Hidden Technical Debt in Machine Learning Systems" was published almost a decade ago. ML Ops has transformed how data science work is delivered, managed, and monitored. Great?

Maybe. In this discussion we cover what is still one of the most glaring gaps in the AI/ML field. Disagreement is accepted and encouraged.