Automated Machine Learning

Bill Vorhies reports on Gartner and Forrester's look at automated machine learning.  Ultimately a key advance for AI and its broad use.  List and analysis of major players.    Click through for outline of advances and Bill's take.

Automated Machine Learning (AML) Comes of Age - Almost   Posted by William Vorhies in DSC

Summary:  Forrester has just released its “New Wave™: Automation-Focused Machine Learning Solutions, Q2 2019” report on leading stand-alone automated machine learning platforms.  This is our first good side-by-side comparison.  You might also want to consider some who were not included.

You know you’ve come of age when the major review publications like Gartner and Forrester publish a study on your segment.  That’s what’s finally happened.  Just released is “The Forrester New Wave™: Automation-Focused Machine Learning Solutions, Q2 2019”.

This is the first reasonably deep review of platforms and covers nine of what Forrester describes as ‘the most significant providers in the segment’.  Those being Aible, Bell Integrator, Big Squid, DataRobot, DMway Analytics, dotData, EdgeVerve, H2O.ai, and Squark.

I’ve been following these automated machine learning (AML) platforms since they emerged.  I wrote first about them in the spring of 2016 under the somewhat scary title “Data Scientists Automated and Unemployed by 2025!  ... " 

Adaptive Machine Learning

Good points in DSC article below on adaptive machine learning.    I often make the case that you should often consider if your modeling should be modeled adaptively.    Data and context can be changing even if we don't expect it.  Time often drives change.  Even if your data is not necessarily streaming.  With an eye towards risk and change management:

Adaptive Machine Learning

Posted by PG Madhavan on May 20, 2016 at 5:30amView Blog
Machine Learning today tends to be “open-loop” – collect tons of data offline, process them in batches and generate insights for eventual action. There is an emerging category of ML business use cases that are called “In-Stream Analytics (ISA)”. Here, the data is processed as soon as it arrives and insights are generated quickly. However, action may be taken offline and the effects of the actions are not immediately incorporated back into the learning process. If we did, it is an example of a “closed-loop” system – we will call this approach “Adaptive Machine Learning” or AML. ISA is a precursor to AML.  .... "