AI-Powered Business Intelligence: Improving Forecasts and Decision Making with Machine Learning

by Tobias Zwingmann

Overview

Use business intelligence to power corporate growth, increase efficiency, and improve corporate decision making. With this practical book featuring hands-on examples in Power BI with basic Python and R code, you'll explore the most relevant AI use cases for BI, including improved forecasting, automated classification, and AI-powered recommendations. And you'll learn how to draw insights from unstructured data sources like text, document, and image files.

Author Tobias Zwingmann helps BI professionals, business analysts, and data analytics understand high-impact areas of artificial intelligence. You'll learn how to leverage popular AI-as-a-service and AutoML platforms to ship enterprise-grade proofs of concept without the help of software engineers or data scientists.

• Learn how AI can generate business impact in BI environments
• Use AutoML for automated classification and improved forecasting
• Implement recommendation services to support decision-making
• Draw insights from text data at scale with NLP services
• Extract information from documents and images with computer vision services
• Build interactive user frontends for AI-powered dashboard prototypes
• Implement an end-to-end case study for building an AI-powered customer analytics dashboard

Product Details

ISBN-13:	9781098111472
Publisher:	O'Reilly Media, Incorporated
Publication date:	07/19/2022
Pages:	389
Product dimensions:	7.00(w) x 9.19(h) x (d)

About the Author

Tobias Zwingmann is an experienced data scientist and cofounder of RAPYD.AI, a Germany-based AI startup focused on prototyping AI solutions. His mission is to help companies adopt machine learning and AI solutions faster while creating meaningful business impact and reducing the risk of project failure. Before founding RAPYD.AI, he worked for more than 15 years in a corporate setting where he was responsible for creating a company-wide data strategy and building out data science use cases.