Gartner Magic Quadrant for Data Science and Machine Learning

KD Nuggets publishes and analyzes the most recent Gartner quadrant analysis. While I am skeptical of this approach, it does have a useful list of participants which can fill in the gaps.   Clip at link below to get to the 'Magic Quadrant'.   Some of the included analysis by KDN is more interesting, with  short, general, non-technical descriptions of what many companies are doing.

The Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms has the largest number of leaders ever. We examine the leaders and changes and trends vs previous years.
By Gregory Piatetsky, KDnuggets.

Gartner has released last week its highly-anticipated report and magic quadrant (MQ) for Data Science and Machine Learning Platforms (DSML) and you can get copies from several vendors - see a list at the bottom of this blog. In previous years, the MQ name kept changing but the 4 leaders remained the same. Now the name has remained the same as in 2019 MQ and 2018 MQ reports, reflecting a more mature understanding of the DSML field, but the contents, especially the leader quadrant, have changed dramatically, reflecting accelerating progress and competition in the field.

The 2020 MQ report went back to evaluating 16 vendors (down from 17 last year), placed as usual in 4 quadrants, based on completeness of vision (vision for short) and ability to execute (ability for short).

We note that the report included only vendors with commercial products, and did not consider open-source platforms like Python and R, even though those are very popular with Data Scientists and Machine Learning professionals.   ... )