# Working with model optimization

You can configure your models to be trained with or without the available optimization features.

## Intelligent model optimization

By default, Qlik Predict uses intelligent model optimization to automatically refine models through iterations on feature selections and by applying advanced transformations. You can turn this feature off for each experiment version if you need more control over the training process.

For more information, see [Intelligent model optimization](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/AutoML/intelligent-model-optimization.htm).

## Hyperparameter optimization

Fine-tune your models using advanced machine learning features.

For more information, see [Hyperparameter optimization](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/AutoML/hyperparameter-optimization.htm).

## Creating time-aware models

For classification and regression models, turn on time-aware training if your training data depends on specific date or time information.

For more information, see [Creating time-aware models](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/AutoML/time-aware-models.htm).

## Learn more

* [Configuring experiments](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/AutoML/configuring-experiments.htm)