Growth in Machine Learning

Machine learning has been used successfully in many disciplines that increasingly depend on it. However, the success relies on human machine learning experts to perform many tasks, according to an account on, a website of the community. These tasks include: Preprocessing and cleaning the data; selecting and constructing appropriate features; selecting an appropriate model family; optimizing model hyper parameters; post-processing machine learning models; and critically analyzing the results.

The growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used more easily and without necessarily expert knowledge. The goal is to progressively automate these manual tasks in what is being called AutoML.

Most companies offering AutoML solutions are positioning them as tools to increase the production of data scientists, and to simplify the process to make it more accessible to new AI developers, according to an account in Towards Data Science written by Justin Tennenbaum, a data scientist.

The machine learning field is trying to move away from “black box” models, and instead are trying simpler models that are easier to interpret. However, AutoML has the potential to exacerbate the problem of whether the model is introducing bias, by hiding the mathematics of the model and performing so many tasks in the background.

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“If there is bias in the real world it can easily be increased by not accounting for it properly in the model,” Tennenbaum stated. “AutoML might cause more harm than good if we use it to fully automate the process. However, full automation is only the goal of a handful of researchers of companies and there is another side which might benefit the community greatly.”

These include tools that help with a quick overview of data and suggestions for what models and techniques are most effective, thus helping the data scientist save time. He cited TPOT, created by Epistasis Labs, an AutoML library in Python built on top of Scikit-Learn, a popular machine learning library.

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Its design is meant to quickly run analysis of the subject data and let the data scientist know which models, features, and parameters are more effective than others.

“TPOT should be thought of as a place to begin or a model comparison and not as a final product,” Tennenbaum stated.

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