The Challenges of Real-Time AI at Scale

Real-time AI at scale is harder than it looks. Pipelines that hum along in development routinely hit problems in production – for example, volatile latencies, stale features reaching the model, and vector index degradation.

This webinar takes a practical look at why such problems occur and how to address them. We'll cover what matters most for real-time AI, walk through a framework for what’s required, and get into specifics on how ScyllaDB addresses real-time AI challenges.

You will learn how to:

• Build pipelines that keep features and embeddings fresh for real-time inference
• Design feature stores for consistent low-latency access at scale
• Maintain predictable vector search performance, even under continuous updates and large datasets