MLOps and AI Utah

Modern ML and AI systems are great at generating information — but they’re surprisingly bad at understanding what enterprise data actually means. Business logic, undocumented assumptions, and tribal knowledge are scattered across one-off SQL and people’s heads — and often never make it into models, agents, or applications at all.

This talk dives into Engineering Meaning: a new systems approach for turning business intent into a first-class, versioned, testable asset — and delivering that meaning in context to AI systems, applications, and analytics.

You’ll see how teams:

Encode business intent into a semantic model as executable meaning

Deliver definitions, relationships, and intent dynamically to agents and applications

Treat context as an engineering problem with observable retrieval and evaluation

Use ML-style feedback loops to continuously improve how meaning is retrieved and applied

Rather than patching prompts, Engineering Meaning moves understanding into a shared system that can be versioned, governed, evaluated, and optimized.

For ML, platform, and data engineers building real production AI systems, this talk frames shared understanding as a missing layer in modern architectures — and shows how treating meaning as infrastructure leads to more reliable AI, faster iteration, and fewer silent failures.

Schedule:
6:15-6:30 food
6:30 pm announcements
6:45-7:30 speaker
7:30-8:30 announcements

Join us on (slack)[https://forgeutah.tech] and if you would like to speak reach to Chris Brousseau or Miriah Peterson

Schedule:
6:15-6:30 food
6:30 pm announcements
6:45-7:30 speaker
7:30-8:30 announcements