You're reading from Power BI Machine Learning and OpenAI

Product type Book

Published in May 2023

Publisher Packt

ISBN-13 9781837636150

Pages 308 pages

Edition 1st Edition

Languages

Python

Concepts

GPT/LLMs

Author (1):

Greg Beaumont

Table of Contents (21) Chapters

Preface

Part 1: Data Exploration and Preparation

Chapter 1: Requirements, Data Modeling, and Planning

Chapter 2: Preparing and Ingesting Data with Power Query

Chapter 3: Exploring Data Using Power BI and Creating a Semantic Model

Chapter 4: Model Data for Machine Learning in Power BI

Part 2: Artificial Intelligence and Machine Learning Visuals and Publishing to the Power BI Service

Chapter 5: Discovering Features Using Analytics and AI Visuals

Chapter 6: Discovering New Features Using R and Python Visuals

Chapter 7: Deploying Data Ingestion and Transformation Components to the Power BI Cloud Service

Part 3: Machine Learning in Power BI

Chapter 8: Building Machine Learning Models with Power BI

Chapter 9: Evaluating Trained and Tested ML Models

Chapter 10: Iterating Power BI ML models

Chapter 11: Applying Power BI ML Models

Part 4: Integrating OpenAI with Power BI

Chapter 12: Use Cases for OpenAI

Chapter 13: Using OpenAI and Azure OpenAI in Power BI Dataflows

Chapter 14: Project Review and Looking Forward

Index

Why subscribe?

Other Books You May Enjoy

Choosing features via data exploration

Your project is to be implemented completely within Power BI, without using external tools. Power BI ML is a software as a service (SaaS) tool that does not require the setup of an infrastructure or advanced coding skills. Traditionally, most ML projects are implemented using highly specialized tools that require strong coding skills with languages such as R and Python. By implementing the entire project in Power BI, you will be able to complete it in a short timeline, build all of the components with SaaS tools and minimal coding, and then manage deployment, scalability, and future changes using a single suite of tools.

The data architecture techniques in this chapter are tailored to analysts and business intelligence developers, and the process will be a great way to learn the basics of finding and modeling features for ML. Experienced ML architects who are fluent in R or Python might handle the process differently, but you need to proceed...