Scaling AI: Designing Systems Built to Grow
11 attendees from 11 groups hosting
Details
The path from prototype to production-grade AI isn’t linear—it’s architectural. As use cases grow more complex and expectations rise, so do the demands on your underlying systems. Scaling AI effectively means designing for modularity, reliability, observability, and performance from day one.
In this webinar, we’ll dig into the technical patterns that support AI at scale. From model deployment strategies and orchestration layers to real-time data pipelines and monitoring practices, we’ll explore how to architect AI systems that don’t buckle under growth—but get better with it.
What You'll Learn:
1️⃣ Scalable Foundations: Core components of an AI architecture that supports increasing complexity without sacrificing speed or reliability.
2️⃣ Modular Design: How to build workflows and services that are loosely coupled, easily governed, and ready for cross-functional collaboration.
3️⃣ Observability in Practice: Techniques for monitoring model behavior, tracking drift, and ensuring end-to-end visibility across the stack.
4️⃣ Speed Without Sacrifice: Strategies to maintain low-latency inference and fast iteration cycles—even as systems scale.