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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

Answers

After putting thought into the questions, compare your answers to ours:

  1. The layers of the Medallion architecture are Bronze, Silver, and Gold.
  2. We recommend DLT build managed pipelines.
  3. In the streaming transactions project example, we used Auto Loader’s schema evolution feature to add a column without manual intervention.
  4. We hope so! One example is a managed streaming data pipeline that could benefit from the built-in data quality monitoring that comes with DLT.
  5. Bucketing is an optimal method specifically designed to provide an additional layer of organization in your data. It can reduce the number of output files and organize the data better for subsequent reading, and it can be especially useful when the partitioning column has high cardinality.
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