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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

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781801812030
Length 244 pages
Edition 1st Edition
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Author (1):
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Debu Sinha Debu Sinha
Author Profile Icon Debu Sinha
Debu Sinha
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Toc

Table of Contents (16) Chapters Close

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 15. Other Books You May Enjoy

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “In the fifth cell, we first initialize some parameters such as our existing username, experiment_name, which is the experiment’s name that’s associated with our AutoML, and the registry_model_name, which will be the model’s name in the Model Registry.”

A block of code is set as follows:

iris = load_iris() X = iris.data  # Features 
y = iris.target  # Labels

Any command-line input or output is written as follows:

from sklearn.datasets import load_iris  # Importing the Iris datasetfrom sklearn.model_selection import train_test_split  # Importing train_test_split function
from sklearn.linear_model import LogisticRegression  # Importing Logistic Regression model

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “To find out which libraries are included in your runtime, you can refer to the System Environment subsection of the Databricks Runtime release notes to check your specific runtime version.”

Tips or important notes

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