Automated Machine Learning vs Data scientists.

AutoML (Automated Machine Learning) automates certain key components of the machine learning pipeline. Some aspects of the machine learning pipeline are automated using AutoML (TPOT, Auto-Sklearn, AutoViML, AutoKeras and many more).

Advantages of AutoML are Accessibility, Efficiency, Fewer Errors, Cost savings, Meet Industry Demands.

But data science jobs would not be eliminated by a lot of reasons.

1.While AutoMLs are capable of selecting models in the majority of cases, they are still unable of doing the majority of a Data Scientist’s labour. We still require data scientists/analysts to apply their domain expertise to develop more meaningful features and information that affect the intended outcome (Feature Engineering).

2.AutoML will not replace most data science professions; rather, it will assist experts in completing their assignments more quickly.

3.Machines aren’t smart enough, and algorithms frequently fail to generalize and comprehend the context of an issue.

4.While AutoML can assist us in finding a good model for a specific problem, it cannot create a novel approach, which is frequently necessary for emergent real-world challenges.

AutoML will continue to grow in popularity. Certain duties will almost certainly be mechanised, and certain responsibilities will almost certainly be eliminated. Because a deep learning model has many parameters, the AutoML framework may speed up a data scientist’s work and deliver the optimal deep learning model in a shorter amount of time.