AutoML-generated models can be quite customizable, but the extent of customization largely depends on the specific AutoML tool being used and the nature of the application. Generally, these tools automate the process of model selection, hyperparameter tuning, and feature engineering, which reduces manual effort for developers. However, many AutoML platforms still allow developers to fine-tune various aspects of the generated models to better suit their specific requirements.
For instance, AutoML tools like Google Cloud AutoML, H2O.ai, and DataRobot provide interfaces where developers can adjust hyperparameters, select performance metrics, and incorporate custom preprocessing steps before training. A developer may want to modify the way features are scaled or impute missing values to fit the data better. Some platforms also support integration of user-defined functions or custom algorithms, enabling developers to incorporate specialized knowledge or specific requirements into the model training process.
It's essential to note that while customization is possible, it might be limited compared to building a model entirely from scratch. AutoML models often operate as black boxes; so, deep insights into the internal workings of the model may not always be available. They can give you control over certain settings, but understanding how these changes impact performance may take some additional effort. Thus, developers should evaluate their needs carefully to decide whether an AutoML tool offers the right balance of automation and customization for their specific use case.