Working with ML predictions
Once you have deployed your machine learning model, you can use the model to create predictions. These predictions can be used to make more efficient and informed decisions based on your data.
You can create and edit ML deployments and generate predictions in personal or shared spaces. You can also publish ML deployments to managed spaces and generate predictions. Access to ML deployments is controlled through the space. For more information about spaces, see Working in spaces.
The following steps are an example of how to work with ML deployments and predictions.
- Deploy your model
Deploy the model you want to use to make predictions.
- Make predictions
Make manual or scheduled predictions on datasets or use the prediction API.
- Visualize the predictive insights
Load the generated prediction data into an app and create visualizations.
- Explore the data with what-if scenarios
Integrate the prediction API into an app to get real-time predictions. This allows you to try out what-if scenarios by changing feature values and getting predicted outcomes for the new values. The record is passed to the ML deployment via API and a response is received in real time. For example, what would happen to the risk of customer churn if we changed the plan type or increased the base fee?
- Take action
Analyze the predictive insights and scenarios to find out which actions to take. Qlik Application Automation helps you automate the actions and provides specific templates for machine learning use cases. For more information about automations, see Qlik Application Automation.