It is no secret that artificial intelligence (AI) and machine learning have advanced radically over the last decade, yet somewhere between better algorithms and faster processors lies the increasingly important task of engineering systems for maximum performance—and producing better results.
The problem for now, says Nidhi Chappell, director of machine learning in the Datacenter Group at Intel, is that "AI experts spend far too much time preprocessing code and data, iterating on models and parameters, waiting for training to converge, and experimenting with deployment models. Each step along the way is either too labor-and/or compute-intensive."
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