Optimize Traffic Management for AI Factory Data Ingest

Harvard Business Review in 2020 outlined how Alibaba affiliate Ant Group creates actionable intelligence from their AI factory to "manage a variety of businesses, serving [over] 10 times as many customers as the largest U.S. banks-with less than one-tenth the number of employees." The way that Ant Group conceptualizes an AI factory build is equally compelling:

"Four components are essential to every factory. The first is the data pipeline, the semiautomated process that gathers, cleans, integrates, and safeguards data in a systematic, sustainable, and scalable way. The second is algorithms, which generate predictions about future states or actions of the business. The third is an experimentation platform, on which hypotheses regarding new algorithms are tested to ensure that their suggestions are having the intended effect. The fourth is infrastructure, the systems that embed this process in software and connect it to internal and external users."

Earlier in our AI factory series, F5 defined an AI factory as a massive storage, networking, and computing investment serving high-volume, high-performance training and inference requirements. This is why the first and forth components in Ant Group's list are especially intriguing: the challenge of establishing the systems needed to safely and efficiently manage data that AI models ingest brings front and center the question of how AI factories should develop the infrastructure around them to produce value.

Traffic management for AI data ingest is the unceasing process through which multi-billion-parameter, media-rich AI data traffic is managed and transported into an AI factory for machine learning and training purposes. This is where a high-performance traffic management solution comes into play to get that traffic into the AI factory. Without such a solution, teams may quickly find themselves needing to re-use connections to keep traffic flowing or hitting storage infrastructure limits, neither of which are ideal for the high-capacity, low-latency data transportation requirements AI factories demand to run at their desired, optimized, pace and scale.