Why AI Code Optimisation Will Be A Game-changer - AI Summary


In practice, most people who've worked with AI or ML in industry know that the technology requires a great deal of manual intervention to be able to smoothly run in a production environment. Whether it be removing redundant lines of code or reordering processes to better use compute or storage resources, scaling AI deployments requires software engineers to dedicate vast amounts of time to parse through models and make hundreds or thousands of individually minute changes. This process is absolutely vital work: whether it be to sense-check code to remove segments that may potentially introduce errors, cut down the risk of memory leaks, or save CPU core cycles and countless kilowatt hours of power consumption. Many companies are thus investing heavily in huge teams of software and hardware engineers to do manual code optimisation to improve the speed of models while not compromising on accuracy. And ultimately, it'll finally make AI viable at scale through taking away the most mundane and repetitive part of the process – and freeing up devs and teams to do far more interesting work.

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