Summary: A year ago we wrote about the emergence of fully automated predictive analytic platforms including some with true One-Click Data-In Model-Out capability. We revisited the five contenders from last year with one new addition and found the automation movement continues to move forward. We also observed some players from last year have now gone in different directions.
Just about a year ago we wrote the original article for which this is the update. Although it was just a year ago it seems like much longer ago that several newly emerged analytic platform vendors were touting fully automated machine learning platforms, the most extreme and interesting of which were literally One-Click Data-In-Model-Out.
With advances in AI and cloud-based massive parallel processing it was apparent that these folks were out to dramatically simplify the process of building deployment-ready models.
Why? Well certainly one driver remains the structural shortage of well trained and affordable data scientists. Hiring is still a problem so trying to do more with fewer DS assets is a reasonable objective. By the way this applies not only to the personnel costs but also the platform costs if you’ve centralized around one of the major platforms like SAS or SPSS. So the premise is that fewer trained data scientists can use these tools to produce the same number and quality of models that required a much larger group much longer to do in the past.
The second driver is the on-going push by some analytic platform vendors to ‘democratize’ predictive analytics, by which they mean to make it possible for the citizen data scientist (aka lesser or untrained amateurs) to build some of these models directly. There are just a whole lot more analysts and citizen data scientists than there are fully trained data scientists and if you want to sell platforms, you’d really like to tap this much larger market. Gartner says this segment will grow 5X more rapidly than the professional data scientist market.
So to accomplish either or both of these objectives you have to:
- Make the process of model building extremely simple (which we know it is not).
- Give reasonably good results very quickly.
- Be very affordable.
If these vendors are successful this may well reduce demand for fully qualified data scientists and makes it reasonable to ask:
Will We Be Automated and Unemployed by 2025?
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