Diverse products complicate data science platforms picture
Data science and machine learning platforms are a varied lot. Vendors offer a diverse set of technologies geared to different parts of the data science process, from collaboration and workflow management hubs to hands-on analytics tools and automated machine learning engines. Comparing the different types of platforms is something of an apples-and-oranges endeavor for prospective users.
But finding the right technologies to meet an organization's analytics needs is a must, particularly as machine learning applications expand and end users who aren't skilled data scientists increasingly look to get involved.
In Gartner's 2019 Magic Quadrant report on platforms for data science and machine learning, analysts Carlie Idoine, Peter Krensky, Erick Brethenoux and Alexander Linden cited the growing role of business analysts and other citizen data scientists in advanced analytics as a key factor. "The ability to collaborate and share is becoming crucial as more users -- in different roles -- adopt data science and [machine learning] platforms," they wrote.
This handbook offers insight into a market that the report's authors said is changing "at unprecedented speed." First, we break down different categories of data science platforms and their uses. Next, Mike Gualtieri, an analyst at Forrester Research, explains the capabilities of automated machine learning platforms in a Q&A. We close with a look at the emerging category of AI-as-a-service platforms and tools.
