The Death of Data Scientists – will AutoML replace them? - KDnuggets


One cannot introduce AutoML without mentioning the machine learning project's life cycle, which includes data cleaning, feature selection/engineering, model selection, parameter optimization, and finally, model validation. As advanced as technology has become, the traditional data science project still incorporates a lot of manual processes and remains time-consuming and repetitive. AutoML came into the picture to automate the entire process from data cleaning to parameter optimization. It provides tremendous value for machine learning projects in terms of both time savings and performance. Launched in 2018, Google Cloud AutoML quickly gained popularity with its user-friendly interface and high performance.

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