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Databricks ML in Action

You're reading from   Databricks ML in Action Learn how Databricks supports the entire ML lifecycle end to end from data ingestion to the model deployment

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781800564893
Length 280 pages
Edition 1st Edition
Languages
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Authors (4):
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Hayley Horn Hayley Horn
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Hayley Horn
Amanda Baker Amanda Baker
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Amanda Baker
Anastasia Prokaieva Anastasia Prokaieva
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Anastasia Prokaieva
Stephanie Rivera Stephanie Rivera
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Stephanie Rivera
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Toc

Table of Contents (13) Chapters Close

Preface 1. Part 1: Overview of the Databricks Unified Data Intelligence Platform FREE CHAPTER
2. Chapter 1: Getting Started and Lakehouse Concepts 3. Chapter 2: Designing Databricks: Day One 4. Chapter 3: Building the Bronze Layer 5. Part 2: Heavily Project Focused
6. Chapter 4: Getting to Know Your Data 7. Chapter 5: Feature Engineering on Databricks 8. Chapter 6: Tools for Model Training and Experimenting 9. Chapter 7: Productionizing ML on Databricks 10. Chapter 8: Monitoring, Evaluating, and More 11. Index 12. Other Books You May Enjoy

Questions

Let’s test ourselves on what we’ve learned by going through the following questions:

  1. How will you use this book? Do you plan to go cover to cover or pick certain sections out? Have you chosen sections of interest?
  2. We covered why transparency in modeling is critical to success. How does Databricks’ glass-box approach to AutoML support this?
  3. Databricks has developed a new way of uniting the open data formats, called UniForm. Which data formats does UniForm unite?
  4. Delta is the foundation of the lakehouse architecture. What is one of the benefits of using Delta?
  5. What is the main advantage of using the Delta file format for large-scale data processing over simple Parquet files in Databricks?
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