Working with experiments

Load historical data into an automated machine learning experiment and train a model to analyze and predict a business problem.

You can create and edit experiments in personal or shared spaces.

Workflow

Before you create an automated machine learning experiment in Qlik Cloud Analytics, you need to have a well-defined machine learning question and a suitable training dataset available in Catalog. For more information, see Defining machine learning questions and Getting your dataset ready for training.

The following steps describe an experiment workflow.

Create your experiment
Create a new experiment in Qlik Sense. Add it to a shared space if you want to work collaboratively.
Creating experiments
Configure your experiment
Select a target to make predictions on and features to support the prediction.
Configuring experiments
Start the training
Start the training of your first experiment version.
Training experiments
Refine the model
During the training, suitable machine learning algorithms are applied to the training data and performance metrics are generated. Review the metrics to see how you can refine the model.
Reviewing models
Adjust parameters such as features and algorithms and retrain new versions of the experiment until you have a good model.
Refining models
Deploy the model
When you have a good model, it’s time to deploy it and start making predictions.
Working with ML deployments

To work with ML experiments, you must have the following:

Professional or Full User entitlement
Automl Experiment Contributor role (to view ML experiments, you can alternatively have the Automl Deployment Contributor role instead)
Required permissions in the space where the experiments are located. You cannot create experiments in a managed space.

For more information, see:

Related learning:

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