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!

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.

There are many ways to integrate Helix QAC static analysis within a continuous integration system like GitLab. Helix QAC's full CI/CD integration accelerates the development pipeline and provides maximum flexibility across the entire organization. In this video, we demonstrate the GitLab and Helix QAC integration with a merge request example. Watch to see how easy it is to compare branches, find and fix issues, and automate CI analysis during key phases of development.

If you’re interested in #apifirst but struggle to understand the organisational or operational drivers that might indicate a positive return on investment for adopting such approaches, then check out this session from Frank Kilcommins and Nauman Ali. They cover the topics above and showcase how visual tools, like the new Form Editor within #Swaggerhub can help break down the barriers to participation in API delivery.

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.

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