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Practical Machine Learning on Databricks

You're reading from Practical Machine Learning on Databricks Seamlessly transition ML models and MLOps on Databricks

Product type Paperback

Published in Nov 2023

Publisher Packt

ISBN-13 9781801812030

Length 244 pages

Edition 1st Edition

Languages

Python

Tools

MLOps

Concepts

Data Science

Author (1):

Debu Sinha

View More author details

Table of Contents (16) Chapters

Preface

1. Part 1: Introduction

2. Chapter 1: The ML Process and Its Challenges FREE CHAPTER

3. Chapter 2: Overview of ML on Databricks

4. Part 2: ML Pipeline Components and Implementation

5. Chapter 3: Utilizing the Feature Store

6. Chapter 4: Understanding MLflow Components on Databricks

7. Chapter 5: Create a Baseline Model Using Databricks AutoML

8. Part 3: ML Governance and Deployment

9. Chapter 6: Model Versioning and Webhooks

10. Chapter 7: Model Deployment Approaches

11. Chapter 8: Automating ML Workflows Using Databricks Jobs

12. Chapter 9: Model Drift Detection and Retraining

13. Chapter 10: Using CI/CD to Automate Model Retraining and Redeployment

14. Index

Why subscribe?

15. Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

Symbols

%pip

utilizing, in notebooks to install notebook-scoped libraries 42, 43

A

access

managing, in Model Registry 100-110

algorithms

supported, by Databricks AutoML 79

Amazon Simple Storage Service (S3) 4

Amazon Web Service (AWS) 14, 20

analysis of variance (ANOVA) 175

Apache Spark 12, 49

application programming interface (API) 64

area under the receiver operating characteristic curve (AUC-ROC) 66

artificial intelligence (AI) 10

Atomicity, Consistency, Isolation, and Durability (ACID) 14, 193

automatic logging (autolog) capabilities 66

AutoML 77

need for 78

reference link 38, 95

running, on churn prediction dataset 83-95

Azure Data Lake Storage (ADLS) 4

B

Bank Customer Churn 99

batch inference 49

ML models, deploying for ...

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Authors (1)

Debu Sinha

Debu Sinha

Debu is an experienced Data Science and Engineering leader with deep expertise in Software Engineering and Solutions Architecture. With over 10 years in the industry, Debu has a proven track record in designing scalable Software Applications, Big Data, and Machine Learning systems. As Lead ML Specialist on the Specialist Solutions Architect team at Databricks, Debu focuses on AI/ML use cases in the cloud and serves as an expert on LLMs, Machine Learning, and MLOps. With prior experience as a startup co-founder, Debu has demonstrated skills in team-building, scaling, and delivering impactful software solutions. An established thought leader, Debu has received multiple awards and regularly speaks at industry events.

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