The cost of “computational debt” in machine learning infrastructure. How to maximize the utilization and scalability of your ML servers
It is not news that machine learning and deep learning is expensive. While the business value of incorporating AI into organizations is extremely high, it often does not offset the computation cost needed to apply these models into your business. Machine learning and deep learning are very compute-intensive, and it has been argued that until cloud or on-premises computing costs decrease — AI innovation will not be worth the cost, despite its unprecedented business value.
In an article on WIRED, Neil Thompson, a research scientist at MIT and author of “The Computational Limits of Deep Learning” mentions numerous organizations from Google to Facebook that have built high-impact, cost-saving models that go unused due to computational cost making the model not profitable. In some recent talks and papers, Thompson says, researchers working on particularly large and cutting-edge AI projects have begun to complain that they cannot test more than one algorithm design, or rerun an experiment because the cost is so high.
Organizations require dramatically more computationally-efficient methods to advance innovation and increase ROI for their AI efforts. Though, high computational cost, and a focus on more efficient computation doesn’t deserve all the blame. In fact, significant advances in GPU-accelerated infrastructure and other cloud providers have dramatically increased the ability to train the most complex AI networks at unprecedented speed. In May 2020, DeepCube released its software-based inference accelerator that drastically improves deep learning performance on any existing hardware. In other words, compute solutions are being developed to meet the increasing demands of machine learning and deep learning. The silent killer of AI innovation is the underutilization of existing compute, and the increasing cost of “computational debt”.
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