As AI moves from experimentation to production, discipline is going to matter just as much as ambition. At LinkedIn we saw a while ago that the cost trends were clear, and that the future of our products would be powered by increasingly compute-expensive AI… more
As AI moves from experimentation to production, discipline is going to matter just as much as ambition. At LinkedIn we saw a while ago that the cost trends were clear, and that the future of our products would be powered by increasingly compute-expensive AI workloads that are more intelligent, more capable, and more useful. But those workloads have to be ROI positive, and that’s not something you can just wake up and do overnight. So we set ourselves a challenge that sounds counterintuitive in today’s environment: how do we deliver increasingly sophisticated AI experiences while keeping our compute footprint as close to flat as possible? This is where owning our own data centers and infrastructure as an applied ML company really pays off, because we can put in the instrumentation and make efficiency a long term investment up and down the stack rather than a one-time push. None of it came from a single breakthrough - it’s the outcome of hundreds of improvements, from optimizing GPU utilization and workload allocation to distilling larger models into smaller and more efficient ones to rethinking how work is distributed across training, inference, storage, and systems design. The results of that intentional push are starting to play out in terms of better experiences for our members and customers while giving our engineers the flexibility to keep innovating. Raghu and I recently sat down with Paresh Dave at WIRED to talk through all of this, check it out! https://lnkd.in/guYj674d1,007 reactions · 41 comments · 26 repostsOpen on LinkedIn
like 926insightful 31celebrate 29love 15support 6