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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

Employing point-in-time lookups for time series feature tables

Time series feature tables are any table in Unity Catalog with a TIMESERIES primary key. These tables are eligible for point-in-time lookups, which is a mechanism for looking up the correct feature values. Before training_sets, coming in Chapter 6, we often joined tables to connect training rows with their feature values. However, a fine-grained event timestamp is not ideal for joining. This led to rounding the timestamps to minutes, hours, or even days. Depending on the use case, this method may or may not work. For example, joining on TransactionTimestamp in Figure 5.3 is not realistic in a standard join so one might create TransactionMinute or TransactionHour.

TransactionTimestamp

TransactionMinute

TransactionHour

2023-09-03 19:23:09.765676

2023-09-03 19:23:00

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