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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

Overview of ML on Databricks

This chapter will give you a fundamental understanding of how to get started with ML on Databricks. The ML workspace is data scientist-friendly and allows rapid ML development by providing out-of-the-box support for popular ML libraries such as TensorFlow, PyTorch, and many more.

We will cover setting up a trial Databricks account and learn about the various ML-specific features available at ML practitioners’ fingertips in the Databricks workspace. You will learn how to start a cluster on Databricks and create a new notebook.

In this chapter, we will cover these main topics:

Setting up a Databricks trial account
Introduction to the ML workspace on Databricks
Exploring the workspace
Exploring clusters
Exploring notebooks
Exploring data
Exploring experiments
Discovering the feature store
Discovering the model registry
Libraries

These topics will cover the essential features to perform effective...

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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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