Real-Time AI at Scale Masterclass

Explore tradeoffs and strategies related to real-time AI at scale – including high-volume feature ingestion, fast retrieval, and low-latency vector search.

This masterclass demonstrates how to keep latency predictably low across common real-time AI use cases. We’ll dig into the challenges behind serving fresh features, handling rapidly evolving embeddings, and maintaining consistent tail latencies at scale. The discussion spans how to build pipelines that support real-time inference, how to model and store high-dimensional vectors efficiently, and how to optimize for throughput and latency under load.

After this free 2-hour masterclass for engineers, architects, and ML/AI practitioners, you will have learned how to:

Build end-to-end pipelines that keep both features and embeddings fresh for real-time inference
Design feature stores that deliver consistent low-latency access at extreme scale
Run vector search workloads with predictable performance—even with large datasets and continuous updates