Yes, AutoML can generate human-readable code for the models it creates. AutoML systems are designed to automate the process of model selection, training, and hyperparameter tuning for machine learning tasks. Many of these systems offer options to export the generated models as code, which makes it easier for developers to review, modify, and integrate the models into their applications.
For example, platforms like Google Cloud AutoML and H2O.ai provide functionalities that allow developers to export their trained models as Python scripts or other programming language representations. This generated code typically includes the necessary functions to preprocess data, make predictions, and assess model performance. Such a feature is beneficial as it gives developers transparency over how the model works and allows them to adapt the code to fit specific needs or optimize it further if required.
Moreover, having access to human-readable code can facilitate collaboration. Developers can review the code generated by AutoML to ensure it meets required standards and integrates well with existing systems. Additionally, this generated code can serve as a learning tool for developers who want to understand the underlying mechanisms of automated model training and deployment. In this way, AutoML not only streamlines the workflow but also provides valuable insights into the modeling process.