What do AutoML tools primarily assist with in machine learning?

AutoML tools primarily assist with making model building easier for non-experts by automating the various stages of the machine learning workflow. These tools provide a user-friendly interface and abstract much of the complexity involved in developing machine learning models.

They enable individuals who may not have extensive technical knowledge or expertise in data science to effectively create, train, and deploy models. AutoML frameworks can automate tasks such as data preprocessing, feature selection, model selection, hyperparameter tuning, and even model evaluation. This democratization of machine learning lowers the barriers to entry, allowing a wider audience to leverage advanced analytics to derive insights and make data-driven decisions.

While optimizing existing models, monitoring performance, and ensuring compliance are important aspects of the machine learning lifecycle, they are not the primary function of AutoML tools. Instead, the core advantage of these tools lies in simplifying the model creation process, focusing on accessibility for users without a strong background in machine learning.