|Photo: Nicole Hemsoth|
|Photo: The Platform|
With everyone from Intel touting the next generation deep learning and machine learning as a partial basis for their Altera buy, to webscale companies like Microsoft, Google, Baidu and others seeking ways to boost machine learning algorithms with hardware, accelerator, and of course, software approaches, the larger conversations tend to get lost in the mix. For instance, what does it mean to optimize for these codes—and what are the system design choices that seem to be the best fits?
|Photo: Joshua Bloom|
Machine learning systems are alive, he says, both “influencing and responding to their environment. At best, they’re valuable, resilient, functioning systems composed of many imperfect parts with many weak contracts between them, built by fallible individuals with broken communication channels, all of whom are living a resource constrained world that’s constantly changing, with the results being consumed by exacting and capricious individuals.” This definition, as he told a group at PyData Seattle, which was hosted by Microsoft, indicates what we already know. This is hard stuff.
The difficulty lies in variability—and that variability means that there are never any standard tradeoffs that suit any algorithms, which is especially true since the same models, once applied to different datasets, can change performance-wise dramatically.
Source: The Platform