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Use Qlik™ Talend Cloud to build a data pipeline that uses Machine Learning for real-time predictions to update data in your Qlik Sense™ application.

In today’s data-driven world, organizations are increasingly leveraging Machine Learning (ML) to extract valuable insights from their data. This powerful technology enables businesses to make data-driven predictions, classify data, and uncover hidden patterns. As a result, ML can provide a significant competitive advantage, improve operational efficiency, and enhance customer experiences. 

However, implementing ML can be challenging. Two key obstacles often arise: poor data source quality and inefficient ML model development. Overcoming these problems requires providing accurate and complete source data for effective ML model training. Also, reducing the time-consuming and resource-intensive effort needed to develop and deploy these models. 

To overcome these challenges, organizations need a streamlined approach to integrate high-quality data with ML capabilities. Qlik™ simplifies this process, making it easier to build data pipelines that leverage the power of ML to drive better business outcomes. 

Introducing Qlik™ AutoML, Qlik™ Application Automation and Qlik™ Talend Cloud 

Qlik™ AutoML automates machine learning by using classification or regression models to find patterns in data that can be used for predictions. Qlik™ AutoML trains and tests your machine learning experiments, making them ready for deployment. These machine learning models can be integrated within Qlik Sense™ applications, Qlik™ Automation workflows and external applications.  

When configuring Qlik™ AutoML experiments, you select the target and features used within the predictive model.  Qlik™ AutoML automatically preprocesses trains and optimizes the model with the use of automatic feature engineering based on your choices.  Once the experiment is complete, your ML models can be deployed through APIs for real-time predictions. Qlik™ AutoML facilitates an iterative workflow by enabling you to tune your model parameters for better optimization.

Qlik™ Application Automation is a powerful tool that enables you to automate data, analytics and ML processes without writing any code. It provides a visual interface where you can easily create and manage automations, consisting of a sequence of actions and event triggers. Automations are a simple way to solve the data consumption use case for ML models where users can choose from templates or build their own workflows by assembling predefined connectors and logical blocks.

Qlik™ Talend Cloud 

Bringing it All Together
Qlik™ Cloud capabilities allow you to use automation to create a workflow that acquires data from any supported source into a target with real-time predictions and load data visualizations within a dashboard application.  

We will demonstrate first using a Titanic passenger survival data set to create and deploy a classification model utilizing AutoML. A data pipeline will be used to ingest and transform source data that can be used for predicting if a passenger would have survived the Titanic. An application automation will be created to invoke the classification model in real-time and update the resultant passenger survivor data in a Qlik Sense™ application. 

 The Qlik™ platform can show an example of providing predictions to a data integration pipeline with the use of automation. (Using the Titanic dataset from Kaggle, we can build a Qlik™ data pipeline that can predict which passengers survived the Titanic.) 

Setting up and running Qlik™ Talend Cloud Services

Build a classification prediction Model using Qlik™ AutoML experiment using the Titanic data set. (Choose deployment model based on F1 score.)