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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

Discussing data preparation

Generally, the first step for any data science project is to explore and prepare the data. We will refer to this process as moving the data from “Bronze” to “Silver” layers in reference to the Medallion architecture methodology. You might think of this type of data transformation exclusively as a data engineering task, but it’s also essential for data science and machine learning.

If you aren’t familiar with this architecture terminology, the Medallion architecture is a data design pattern used to organize data logically in a warehouse. This architecture is also commonly called “multi-hop” architecture. It aims to incrementally and progressively improve the structure and quality of data as it flows through each layer. The Medallion architecture has three layers: Bronze, Silver, and Gold, listed as follows:

  • The Bronze layer is the raw data layer. It contains all the raw, unprocessed data ingested...
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