Using Qlik AutoML in a cloud environment
There are several steps when deploying a machine learning model using Qlik AutoML. These steps are illustrated in the following diagram:
Figure 8.1: The AutoML workflow
As you might remember from our earlier chapters, the first step of every machine learning project is to define a business problem and question, followed by the steps required for data cleaning, preparation, and modeling. Typically, data cleaning and transformation part can take up 80–90% of the time spent on a project.
Once we have a machine-learning-ready dataset, we will continue by creating a machine learning experiment.
In automated machine learning, the process of training machine learning algorithms on a specific dataset and target is automated. When you create an experiment and load your dataset, the system automatically examines and prepares data for machine learning. It provides you with statistics and insights about each column...
