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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

Product type Paperback

Published in May 2024

Publisher Packt

ISBN-13 9781800564893

Length 280 pages

Edition 1st Edition

Languages

Python

Tools

Spark NLP

Concepts

Machine Learning

Authors (4):

Hayley Horn

Amanda Baker

Anastasia Prokaieva

Stephanie Rivera

View More author details

Table of Contents (13) Chapters

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

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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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Authors (4)

Stephanie Rivera

Stephanie Rivera

Stephanie Rivera has worked in big data and machine learning for 12 years. She collaborates with teams and companies as they design their Lakehouse as a Sr. Solutions Architect for Databricks. Previously Stephanie was the VP, Data Intelligence for a global company, taking in 20+ terabytes of data daily. She led the data science, data engineering, and business intelligence teams.

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Hayley Horn

Hayley Horn

Hayley Horn started her data career 15 years ago as a data quality consultant on enterprise data integration projects. As a data scientist, she specialized in customer insights and strategy, and presented at Data Science and AI conferences in the US and Europe. She is currently a Sr. Solutions Architect for Databricks, with expertise in data science and technology modernization. A graduate of the MS Data Science program at Southern Methodist University in Dallas, Texas, USA, she is now a capstone advisor to students in their final semesters of the program.

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Amanda Baker

Amanda Baker

Mandy Baker began her career in data 8 years ago. She loves leveraging her skills as a data scientist to orchestrate transformative journeys for companies across diverse industries as a Solutions Architect for Databricks. Her experiences have brought her from large corporations to small startups and everything in between. Mandy is a graduate of Carnegie Mellon University and the University of Washington.

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Anastasia Prokaieva

Anastasia Prokaieva

Anastasia Prokaieva began her career 9 years ago as a research scientist at CEA (France), focusing on large data analysis and satellite data assimilation, treating terabytes of data. She has been working within the big data analysis and machine learning domain since then. In 2021, she joined Databricks and became the regional AI subject matter expert. On a daily basis, Anastasia consults Databricks users on best practices for implementing AI projects end-to-end. She also delivers training and workshops to democratize AI. Anastasia holds two MSc degrees in theoretical physics and energy science.

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