Machine learning with Qlik Predict
Automated machine learning finds patterns in your data and uses them to make predictions on future data. Machine learning experiments in Qlik Cloud Analytics let you collaborate with other users and integrate your predictive analytics in Qlik Sense apps. In addition to making predictions, you can do an in-depth analysis of the key features that influence the predicted outcome.
Load historical data from the catalog, start the automated machine learning process, and then choose the best-fitting machine learning model for your use case. Deploy the models to make predictions on the outcome of business problems. Explore the variables that impact the predicted outcome, and gain a thorough understanding of your data.
Alternatively, developers can integrate Qlik Predict capabilities into their own workflows using the Machine Learning API. For a tutorial to help you get started, see Automated machine learning tutorial.
Qlik Cloud Government does not support Qlik Predict.
Getting started with machine learning
Understanding machine learning
Learn about the basic concepts behind machine learning, and why you might want to use it.
Defining machine learning questions
Learn about how to define your machine learning question and follow the structured framework.
Preparing a training dataset
Learn about how to prepare your dataset for use in training machine learning models.
Working with experiments
Working with ML experiments
Get an overview of the automated machine learning process and start creating experiments.
Working with time series experiments
Learn about how to train models to perform time-specific forecasting.
Interpreting model performance
Learn about the model metrics that are available for scoring predictive models.
Refining models
How can you improve your predictive model? Learn more here.
Working with ML deployments
Deploying models
When you have produced a model that is ready for predictions on new data, deploy it into an ML deployment.
Working with ML deployments
Learn about deploying models, managing ML deployments, and activating deployed models for predictions.
Working with predictions
Working with predictions
Learn how to use your ML deployment to create predictions using the interface or the API.
Creating batch predictions
Use the ML deployment interface to generate datasets with predictive data.
Generating SHAP datasets during predictions
Understand how to use SHAP values to understand the key drivers behind your data as you make predictions.
Creating real-time predictions
Learn how to access and use the Machine Learning API for generating real-time predictions on one or a handful of rows of data.
Predicting with the Qlik Predict analytics connector
Use the Qlik Predict analytics connector to communicate with your deployment and make predictions directly in apps and scripts.
Hands-on tutorials and guides
Tutorial – Generating and visualizing prediction data
This tutorial shows you how to create and train an experiment, deploy a model and generate predictions, and visualize the prediction data in a Qlik Sense app.
Tutorial — Predicting sales with multivariate time series forecasting
This tutorial walks you through the process of training, deploying, and predicting with models that can perform time-specific forecasts.
Qlik Predict videos
Watch some of our short videos to get started with machine learning.