The incredible advances in machine learning research in recent years often take time to propagate out into usage in the field. One reason for this is that such βstate-of-the-artβ results for machine learning performance rely on the use of handwritten, idiosyncratic optimizations for specific hardware models or operating contexts. When developers are building ML-powered systems
The post OctoML: Automated Deep Learning Engineering with Jason Knight and Luis Ceze appeared first on Software Engineering Daily.
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