You're reading from Mastering Azure Machine Learning Execute large-scale end-to-end machine learning with Azure

Product type Paperback

Published in May 2022

Publisher Packt

ISBN-13 9781803232416

Length 624 pages

Edition 2nd Edition

Tools

Azure

Concepts

Machine Learning

Authors (2):

Körner

Alsdorf

View More author details

Table of Contents (23) Chapters

Preface

1. Section 1: Introduction to Azure Machine Learning

2. Chapter 1: Understanding the End-to-End Machine Learning Process FREE CHAPTER

3. Chapter 2: Choosing the Right Machine Learning Service in Azure

4. Chapter 3: Preparing the Azure Machine Learning Workspace

5. Section 2: Data Ingestion, Preparation, Feature Engineering, and Pipelining

6. Chapter 4: Ingesting Data and Managing Datasets

7. Chapter 5: Performing Data Analysis and Visualization

8. Chapter 6: Feature Engineering and Labeling

9. Chapter 7: Advanced Feature Extraction with NLP

10. Chapter 8: Azure Machine Learning Pipelines

11. Section 3: The Training and Optimization of Machine Learning Models

12. Chapter 9: Building ML Models Using Azure Machine Learning

13. Chapter 10: Training Deep Neural Networks on Azure

14. Chapter 11: Hyperparameter Tuning and Automated Machine Learning

15. Chapter 12: Distributed Machine Learning on Azure

16. Chapter 13: Building a Recommendation Engine in Azure

17. Section 4: Machine Learning Model Deployment and Operations

18. Chapter 14: Model Deployment, Endpoints, and Operations

19. Chapter 15: Model Interoperability, Hardware Optimization, and Integrations

20. Chapter 16: Bringing Models into Production with MLOps

21. Chapter 17: Preparing for a Successful ML Journey

22. Other Books You May Enjoy

Technical requirements

In this chapter, we will use the following Python libraries and versions to create decision tree-based ensemble classifiers:

azureml-core 1.34.0
azureml-sdk 1.34.0
lightgbm 3.2.1
numpy 1.19.5
pandas 1.3.2
scikit-learn 0.24.2
seaborn 0.11.2
matplotlib 3.4.3

Similar to previous chapters, you can execute this code using either a local Python interpreter or a notebook environment hosted in Azure Machine Learning.

All code examples in this chapter can be found in the GitHub repository for this book: https://github.com/PacktPublishing/Mastering-Azure-Machine-Learning-Second-Edition/tree/main/chapter09.

The rest of the chapter is locked

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You're reading from Mastering Azure Machine Learning Execute large-scale end-to-end machine learning with Azure

Table of Contents (23) Chapters

Technical requirements

Authors (2)

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