AI & ML Research 3 Hours

Last updated: April 15, 2026, 11:30 AM ET

ML Inference & Data Engineering

Architectural shifts in large language model inference, specifically disaggregating compute, offer potential cost reductions of two- to four-fold by recognizing that the prefill stage is compute-bound while the decoding stage is memory-bound, a configuration many ML teams have yet to implement. Concurrently, data practitioners attempting to modernize infrastructure must carefully transition batch pipelines to real-time operations, a move requiring specific architectural considerations that will be detailed in an upcoming technical webinar. Expanding beyond traditional media, developments in data compression are now focusing on generalized representations, suggesting the future of compression will encompass heterogeneous data types, including biological sequences like DNA, rather than solely focusing on audio and video codecs.