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Power BI Machine Learning and OpenAI

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
Concepts
Author (1):
Greg Beaumont Greg Beaumont
Profile icon 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 Other Books You May Enjoy

Summary

In this chapter, you reviewed each of the ML models that you have built. You decided to seek guidance on the next steps for the Predict Damage ML model from either a data science team or your stakeholders. For the Predict Size ML model, you found only slight predictive value and will need to seek guidance for your next course of action. The Predict Height ML model improved when you added new filter criteria and whittled down the feature selection, and the results are promising. At this point, you must either work with a data science team or circle back with your stakeholders for guidance on future plans for the model.

In Chapter 11, you will bring in newly added data from the FAA Wildlife Strike database and run it through your Predict Damage ML model to test the results. In doing so, you will learn how to score new data with your ML model whenever data refreshes in Power BI. You will also explore opportunities to find new value by adding Microsoft OpenAI capabilities to...

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