Every team developing AI applications faces the same challenge: moving fast with data while keeping governance intact.
Based on our work with customers adopting AI data infrastructure, this webinar shares the lessons we’ve learned from enterprises that have successfully balanced model velocity with strong governance.
Learn why governance efforts often fail to gain traction with data scientists and ML engineers, and how leading teams make the governed path the easiest path instead of an obstacle.
Watch this webinar and learn
- Why after-the-fact governance fails, and how it creates friction between compliance and AI development
- What data scientists, ML engineers, and governance teams are each optimizing for, and how to align those priorities
- How to build governance into workflows with patterns like branch protection, approvals, and audit trails
- How to validate end-to-end before scaling governed workflows across the organization
