Book Image

Machine Learning with Qlik Sense

By : Hannu Ranta
Book Image

Machine Learning with Qlik Sense

By: Hannu Ranta

Overview of this book

The ability to forecast future trends through data prediction, coupled with the integration of ML and AI, has become indispensable to global enterprises. Qlik, with its extensive machine learning capabilities, stands out as a leading analytics platform enabling businesses to achieve exhaustive comprehension of their data. This book helps you maximize these capabilities by using hands-on illustrations to improve your ability to make data-driven decisions. You’ll begin by cultivating an understanding of machine learning concepts and algorithms, and build a foundation that paves the way for subsequent chapters. The book then helps you navigate through the process of framing machine learning challenges and validating model performance. Through the lens of Qlik Sense, you'll explore data preprocessing and analysis techniques, as well as find out how to translate these techniques into pragmatic machine learning solutions. The concluding chapters will help you get to grips with advanced data visualization methods to facilitate a clearer presentation of findings, complemented by an array of real-world instances to bolster your skillset. By the end of this book, you’ll have mastered the art of machine learning using Qlik tools and be able to take your data analytics journey to new heights.
Table of Contents (17 chapters)
Part 1:Concepts of Machine Learning
Part 2: Machine learning algorithms and models with Qlik
Part 3: Case studies and best practices


In this chapter, we took a closer look at model creation and deployment using Advanced Analytics Integration and the server-side R extension in an on-premises environment (having done the initial environment setup in Chapter 5).

We started our journey in this chapter by getting familiar with the two concepts of utilizing Advanced Analytics Integration. We then took a closer look at an on-the-fly data analytics use case and created a k-means clustering example with real-time integration with R.

We built a simple dashboard to support our analysis and took a deeper look at the Advanced Analytics Integration syntax. In the latter part of this chapter, we learned how to debug and monitor our models running in on-premises environments.

In the next chapter, we will shift our focus toward Qlik AutoML. We will learn the implementation model used with AutoML and how to utilize this tool both in Qlik Cloud and on-premises. We will also learn how to deploy and monitor models using...