Which AutoML feature is NOT found in the Data Science Platform?

Automatic data visualization is typically not a core feature included in many AutoML platforms. While data visualization is an important aspect of data analysis and presentation, AutoML focuses primarily on automating the processes directly related to model development, which includes hyper-parameter tuning, model evaluation, and model explanation.

Hyper-parameter tuning optimizes the various parameters that govern how a model is trained, enabling the model to improve its accuracy and performance. Model evaluation assesses how well the trained model performs on unseen data, ensuring that the model generalizes well beyond the training dataset. Model explanation provides insights into how the model makes predictions, which is vital for accountability and understanding model behavior.

While automatic data visualization can be beneficial for communicating insights and trends within the data, it is not a feature that typically falls under the direct responsibilities of an AutoML system focused on model training and deployment.