This video shows you how to use the predictive model data created in video 1 to create a very simple predictive Qlik Sense App.

Join us in this engaging webinar as we examine the role of AI in automating invoice payments within the retail landscape. We will highlight the significance of data extraction technologies and their ability to enhance payment accuracy and speed. Learn about the challenges faced by retailers and how AI solutions can address these issues effectively.

Predict, Explain, Act with Qlik AutoML, a powerful tool that brings automated machine learning to the hands of business users and data analysts. In this all new series, revist and learn how Qlik AutoML allows you to build predictive models without needing deep technical expertise in data science. Follow along in the next few videos linked in the description as Mike Tarallo walks you through the key features, from experiment to deployment, and see how Explain-ability is defined and used to gain insights that drive decision-making. Perfect for those looking to enhance their analytics with AI-powered predictions!

See how easy it is to create a Qlik AutoML Predictive Model.

In this video, we'll guide you through the process of connecting to Microsoft Azure SQL Server using Astera Data Stack. Users can connect to Azure SQL Databases using various objects, including Database Table Source, Database Table Destination, and SQL-related tasks. Contents of the video: Introduction to connecting with Microsoft Azure SQL Server.

In this video, we will learn access Astera's Data Governance Platform.

In this video, we will learn how to deploy a data asset in Astera Data Stack to govern them in Astera's Data Governance Platform.

Shift Left is a rethink of how we circulate, share and manage data in our organizations using DataStreams, Change Data Capture, FlinkSQL and Tableflow. It addresses the challenges with multi-hop and medallion architectures using batch pipelines by shifting the data preparation, cleaning and schemas to the point where data is created and as a result, you can build fresh trustworthy datasets as streams for operational use cases or Apache Iceberg tables for analytical use cases.