The successful adoption of automation technology significantly reduces manual tasks. It manages infrastructure and frees up human workforces to focus on valuable tasks. AutoML, or automated machine learning, does the same for machine learning. AutoML typically refers to the ability to automate the process of integrating machine learning into real-world problems, eliminating the need for data scientists. But it doesn’t mean it is going to replace data scientists as it has become a common question when it comes to AutoML. It will, however, help power data science workflows and assist data scientists and let them focus on investing their valuable time on tasks that are more challenging, creative and difficult to automate.

For modern machine learning systems, it is quite common to hear people refer to it as “black boxes”. However, in the last few years, the systems have begun diverging away from it and instead using simpler models that are easier to construe. As complex models can be hard to interpret, it is difficult to know when a model is introducing bias. This is where AutoML comes in, exacerbating the black-box model’s problem by hiding the mathematics of the model and performing data cleaning, feature selection, model selection, parameter selection.

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