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“Machine learning costs, talent and chip shortages… any AI and machine learning company faces at least one of these challenges, and most face a few at a time,” Pekhimenko told TechCrunch in an email interview. Microsoft is facing a shortage of the server hardware needed to run AI so severe that it might lead to service disruptions, the company warned in a summer earnings report. ’s led some companies, including OpenAI, Google, AWS, Meta and Microsoft, to build — or explore building — their own custom chips for model training. The platform attempts to identify bottlenecks during model training and predict the total time and cost to deploy a model. Beyond this, CentML provides access to a compiler — a component that translates a programming language’s source code into machine code that hardware like a GPU can understand — to automatically optimize model training workloads to perform best on target hardware. CentML isn’t the first to take a software-based approach to model optimization.
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