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Power BI Machine Learning and OpenAI

You're reading from   Power BI Machine Learning and OpenAI Explore data through business intelligence, predictive analytics, and text generation

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781837636150
Length 308 pages
Edition 1st Edition
Languages
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Author (1):
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Greg Beaumont Greg Beaumont
Author Profile Icon Greg Beaumont
Greg Beaumont
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Data Exploration and Preparation
2. Chapter 1: Requirements, Data Modeling, and Planning FREE CHAPTER 3. Chapter 2: Preparing and Ingesting Data with Power Query 4. Chapter 3: Exploring Data Using Power BI and Creating a Semantic Model 5. Chapter 4: Model Data for Machine Learning in Power BI 6. Part 2: Artificial Intelligence and Machine Learning Visuals and Publishing to the Power BI Service
7. Chapter 5: Discovering Features Using Analytics and AI Visuals 8. Chapter 6: Discovering New Features Using R and Python Visuals 9. Chapter 7: Deploying Data Ingestion and Transformation Components to the Power BI Cloud Service 10. Part 3: Machine Learning in Power BI
11. Chapter 8: Building Machine Learning Models with Power BI 12. Chapter 9: Evaluating Trained and Tested ML Models 13. Chapter 10: Iterating Power BI ML models 14. Chapter 11: Applying Power BI ML Models 15. Part 4: Integrating OpenAI with Power BI
16. Chapter 12: Use Cases for OpenAI 17. Chapter 13: Using OpenAI and Azure OpenAI in Power BI Dataflows 18. Chapter 14: Project Review and Looking Forward 19. Index 20. 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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