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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

Why subscribe?

12. Other Books You May Enjoy

Questions

The following questions are meant to solidify key points to remember as well as tie the content back to your own experience:

What are some low-code options for data exploration that we discussed in this chapter?
When might you use Databricks Assistant for data exploration, and when might you use AutoML’s data profile notebook?
How and why would you set expectations on your data?
When would you use a regular database versus a vector database? What are some common use cases for vector databases?

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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.

See other products by Stephanie Rivera

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.

See other products by Hayley Horn

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.

See other products by Amanda Baker

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.

See other products by Anastasia Prokaieva

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