The Next Battleground for Deep Learning Performance

The frameworks are in place, the hardware infrastructure is robust, but what has been keeping machine learning performance at bay has far less to do with the system-level capabilities and more to do with intense model optimization. While it might not be the sexy story that generates the unending wave of headlines around deep learning, hyperparameter tuning is a big barrier when it comes to new leaps in deep learning performance. In more traditional machine learning, there are plenty of open sources tools for this, but where it is needed most is in deep learning--an area that does appear to be gaining a solid enterprise foothold outside of the initial web companies that spun services based on image, speech, and video recognition. Optimizing traditional machine learning and newer deep learning frameworks like TensorFlow is not simple--and it can have an incredible impact when it is done (or not done) well, providing many orders of magnitude improvements in accuracy, performance, or efficiency--depending on what users tune for. Configuring around the number and scope of hypermeters in a TensorFlow-driven workload leaves humans in the dust and optimizing with brute force methods is computationally wasteful, at least if there is a more targeted, streamlined way of knob-turning for the desired model modifications (performance, accuracy, etc.).