Here at The Next Platform, we've touched on the convergence of machine learning, HPC, and enterprise requirements looking at ways that vendors are trying to reduce the barriers to enable enterprises to leverage AI and machine learning to better address the rapid changes brought about by such emerging trends as the cloud, edge computing and mobility. At the SC17 show in November 2017, Dell EMC unveiled efforts underway to bring AI, machine learning and deep learning into the mainstream, similar to how the company and other vendors in recent years have been working to make it easier for enterprises to adopt HPC techniques for their environments. For Dell EMC, that means in part doing so through bundled, engineered systems. IBM has strategies underway, including through the integration of its PowerAI deep learning enterprise software with its Data Science Experience. Both offerings are aimed at making it easier for enterprises to embrace advance AI technologies and for developers and data scientists to develop and train machine learning models.

Here at The Next Platform, we've touched on the convergence of machine learning, HPC, and enterprise requirements looking at ways that vendors are trying to reduce the barriers to enable enterprises to leverage AI and machine learning to better address the rapid changes brought about by such emerging trends as the cloud, edge computing and mobility. At the SC17 show in November 2017, Dell EMC unveiled efforts underway to bring AI, machine learning and deep learning into the mainstream, similar to how the company and other vendors in recent years have been working to make it easier for enterprises to adopt HPC techniques for their environments. For Dell EMC, that means in part doing so through bundled, engineered systems. IBM has strategies underway, including through the integration of its PowerAI deep learning enterprise software with its Data Science Experience. Both offerings are aimed at making it easier for enterprises to embrace advance AI technologies and for developers and data scientists to develop and train machine learning models.

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.

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