Mainstreaming Machine Learning: Emerging Solutions

In the course of this three-part series on the challenges and opportunities for enterprise machine learning, we have worked to define the landscape and ecosystem for these workloads in large-scale business settings and have taken an in-depth look at some of the roadblocks on the path to more mainstream machine learning applications. In this final part of the series, we will turn from pointing to the problems and look at the ways the barriers can be removed, both in terms of leveraging the technology ecosystem around machine learning and addressing more difficult problems, most notably, how to implement the human side of machine learning in an organization. For now, however, let's start looking at solutions at the top of the technology side with the sheer performance and workflow possibilities. Logically, if we want to reduce the cycle time for machine learning radically, it makes sense to attack the most time-consuming tasks. As we noted previously, data scientists spend most of their time collecting and cleaning data, so it makes sense to focus effort on simplifying and expediting this task.