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)
1
Part 1:Concepts of Machine Learning
6
Part 2: Machine learning algorithms and models with Qlik
12
Part 3: Case studies and best practices

Deploying and Monitoring Machine Learning Models

In previous chapters, we learned a lot about different models and techniques. Understanding the concepts and building a machine learning model is only the beginning of the journey toward realizing its true value. The successful deployment and ongoing monitoring of these models are crucial to ensuring their effectiveness and reliability in real-world scenarios.

Ensuring that a model performs optimally, seamlessly integrates with existing systems, and adapts to evolving requirements requires a comprehensive understanding of the deployment process and the associated considerations. In the context of the Qlik platform, most of the typical pain points are handled by the platform itself and the design of the components, but there are still things we have to bear in mind.

Once a machine learning model is deployed, it is vital to continuously monitor its performance to identify potential issues, maintain accuracy, and safeguard against...