Summary:\u00a0<\/em><\/strong> AML has been around since at least 2016 but only in the last year have Gartner and Forrester begun to offer their opinions.\u00a0 Here\u2019s where we stand.<\/em><\/p>\n \u00a0<\/p>\n And yet something keeps bugging me.\u00a0 I\u2019ve been following this trend since its emergence in about 2016 and there\u2019s still no good single source to turn to for comprehensive reviews and comparisons.\u00a0 Another way of saying this is that if Forrester and Gartner haven\u2019t reviewed AML, does this really represent something to pay attention to.<\/p>\n \u00a0<\/p>\n The Forrester New Wave\u2122: Automation-Focused Machine Learning Solutions, Q2 2019<\/strong><\/span><\/p>\n We were excited earlier in the year when Forrester published its first ever AML review<\/a><\/u><\/em>.\u00a0 They picked some winners and losers and gave some good guidance for comparison.<\/p>\n \u00a0 While the analysis was certainly up to Forrester\u2019s high professional standards it was disturbing that there were many high ranked players I had never even heard of. \u00a0Turns out participants were not selected based on some broad industry survey but from among those Forrester thought were leaders and who had submitted an RFP-like response.\u00a0 Research was limited to a 1 \u00bd hr. interview \/ demo and calls to some of their installed customers.\u00a0 Inclusion criteria required the vendor to have at least five installed customers and have a full spectrum AML offering.\u00a0<\/p>\n So the vendors who participated were to an extent self-selected and there\u2019s no indication of who might have applied and been rejected.\u00a0 I suspect there are a number of other competitors who could have passed these hurdles.<\/p>\n Well, some information is better than none so long as readers see that the field of competitors reviewed is not altogether objective.<\/p>\n \u00a0<\/p>\n What about Gartner?<\/strong><\/span><\/p>\n So I was excited to see a recent report from Gartner \u201cHow Augmented Machine Learning is Democratizing Data Science<\/a><\/u><\/em>\u201d hoping this might be the first in a much awaited review of AML competitors.\u00a0 Unfortunately it is not.<\/p>\n The report covers the generalities of AML platforms that can be used by professionals, proceeding on by category until arriving at the most completely automated like DataRobot and H2O.ai.\u00a0<\/p>\n Gartner also seems insistent on applying its own naming convention referring to the category as \u2018augmented\u2019 machine learning which really isn\u2019t going to help the reader gain clarity since \u2018automated machine learning\u2019 is already the much more common phrase.<\/p>\n There are a couple of observations here worth pointing out.<\/p>\n Perhaps most interesting, and I wonder if this is an oversight in the report, is that their interpretation of AML includes the automation of deep learning models (ADL) for image and text.\u00a0 There are a handful of current AML competitors able to do this but it\u2019s unclear how much customers want this bulked up capability.\u00a0<\/p>\n See our recent analysis of Booz Allen\u2019s Modzy<\/a><\/u><\/em> launch as perhaps a more efficient path to providing DNN models to those who need them.\u00a0 Let\u2019s be honest, probably 90% of the value being created by models comes from our well understood ML techniques and not the unusual problems that rely on text, voice, and image classification.<\/p>\n \u00a0<\/p>\n Who Do Forrester and Gartner Think Should be Considered<\/strong><\/p>\n Neither of these reports is based on a comprehensive market penetration survey but it\u2019s interesting to see the overlap.<\/p>\n<\/a>This has been a big year for AML (automated machine learning).\u00a0 A number of new players have emerged and pretty much everyone acknowledges that some level of automation is appropriate to enhance the productivity of your data science team.\u00a0 And no, data scientists shouldn\u2019t be alarmed by this trend.\u00a0 None of this approaches letting an untrained person push the button and have a useful model pop out.<\/p>\n
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