BIBFRAME Work

Title

Artificial intelligence with Microsoft Power BI

Type

  • Monograph
  • Text

Contribution

Subject

  • c8bc3ef4-bd2f-46ea-a38e-fa53abff46ae
  • Business Intelligence (GND)
  • Microsoft (GND)
  • Künstliche Intelligenz (GND)
  • Artificial intelligence (LCSH)
  • Power BI (GND)
  • Unternehmen (GND)
  • artificial intelligence (AAT)
  • Intelligence artificielle (RVM)
  • Microsoft Power BI (Computer file)
  • Artificial intelligence

Language

Illustrative Content

Classification

Content

Supplementary content

Summary

Advance your Power BI skills by adding AI to your repertoire at a practice level. With this practical book, business-oriented data analysts and developers will learn the terminologies, practices, and strategy necessary to successfully incorporate AI into your business intelligence estate. Jennifer Stirrup, CEO of AI and BI leadership consultancy Data Relish, and Thomas Weinandy, research economist at Upside, show you how to use data already available to your organization. Springboarding from the skills that you already possess, this book adds AI to your organization's technical capability and expertise with Microsoft Power BI. By using your conceptual knowledge of BI, you'll learn how to choose the right model for your AI work and identify its value and validity.

Table of Contents

Getting started with AI in the enterprise: your data -- A great foundation: AI and data modeling -- Blueprint for AI in the enterprise -- Automating data exploration and editing -- Working with time series data -- Cluster analysis and segmentation -- Diving deeper: using Azure AI services -- Text analytics -- Image tagging -- Custom machine learning models -- Data science languages: Python and R in Power BI -- Making your AI production-ready with Power BI -- The AI feedback loop.

Authorized Access Point

Stirrup, Jennifer. Artificial intelligence with Microsoft Power BI

Admin Metadata

  • Date: 2026-09-11T20:16:02.085276+00:00
  • Agent: cbc
  • Status: c
  • Derived from: 24024077

Admin Metadata

  • Date: 2026-09-11
  • Cataloger id: Unknown
  • Description level: http://id.loc.gov/ontologies/bibframe-2-6-0/
  • Description language: eng
  • Description authentication: pcc

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