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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

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15. Other Books You May Enjoy

Libraries

Libraries are fundamental building blocks of any programming ecosystem. They are akin to toolboxes, comprising pre-compiled routines that offer enhanced functionality and assist in optimizing code efficiency. In Databricks, libraries are used to make third-party or custom code available to notebooks and jobs running on clusters. These libraries can be written in various languages, including Python, Java, Scala, and R.

Storing libraries

When it comes to storage, libraries uploaded using the library UI are stored in the Databricks File System (DBFS) root. However, all workspace users can modify data and files stored in the DBFS root. If a more secure storage option is desired, you can opt to store libraries in cloud object storage, use library package repositories, or upload libraries to workspace files.

Managing libraries

Library management in Databricks can be handled via three different interfaces: the workspace UI, the command-line interface (CLI), or the Libraries...

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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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