Automated Machine Learning to Streamline Data-Driven Industrial Application Development

The emergence of the fourth industrial revolution has resulted in a fundamental change in the way organisations function, highlighting the need to make decisions based on data. Automated Machine Learning (AutoML) has become a key tool in this revolution, allowing quick model building and deployment without data science skills. This article examines how AutoML simplifying data-driven industrial application development affects efficiency, precision, and scalability. AutoML basics and industrial applications. AutoML tools automate data preparation, feature selection, model selection, hyperparameter tweaking, and model assessment for machine learning. Automation saves time and effort in developing effective ML models. Compared to more conventional white-box modelling methods, machine learning has often been shown to be the better choice in practical industrial settings. Still, development-related human variables reduce the predictability of locating a solution near the theoretical optimum. In contrast, Hyperparameter optimisation, model selection, and feature extraction may all be fully automated using automated machine learning (Auto ML). The article shows how AutoML can simplify the creation of data-driven industrial apps using a well-known open dataset. AutoML is a breakthrough tool that can expedite data-driven industrial application development. AutoML democratises sophisticated analytics and helps enterprises maximise data to use for smarter, more effective operations by automating difficult machine learning procedures.