Modern AI systems are increasingly complex, requiring seamless integration of diverse components, robust governance, and scalable infrastructure.

Compound AI Systems tackle AI tasks using multiple interacting components, processes, and tools.

This session will introduce a structured approach to designing, deploying, and managing enterprise-grade AI solutions using Union’s reference architecture. Attendees will learn how to unify modular components into cohesive systems while addressing critical challenges like collaboration, cost visibility, and performance.

The session will begin with an overview of the reference architecture, highlighting its core pillars:

Actors: long-running “warm” containers for extremely fast executions.
Artifacts: Managing inputs, outputs, and reusable assets (datasets, model versions, pipelines) with traceability.
Serving: Deploying scalable inference endpoints and APIs while optimizing latency, cost, and reliability.
Cost monitoring: Identify resource consumption and cost allocation.
Live Debugging: Monitoring systems in production, diagnosing failures, and iterating without downtime.

A live demo will bring these concepts to life, showcasing how the Union platform enables teams to build, govern, and refine compound AI systems end-to-end.

Whether you’re an ML engineer, architect, or platform lead, this session will equip you with actionable strategies to simplify complexity, enforce governance, and accelerate time-to-value in AI initiatives.

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