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Mastering Azure Machine Learning

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

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Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781803232416
Length 624 pages
Edition 2nd Edition
Tools
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Authors (2):
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Marcel Alsdorf Marcel Alsdorf
Author Profile Icon Marcel Alsdorf
Marcel Alsdorf
Christoph Körner Christoph Körner
Author Profile Icon Christoph Körner
Christoph Körner
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Toc

Table of Contents (23) Chapters Close

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

Training an ensemble classifier model using LightGBM

Both random forest and gradient boosted trees are powerful ML techniques due to the simplicity of decision trees and the benefits of combining multiple classifiers. In this example, we will use the popular LightGBM library from Microsoft to implement both techniques on a test dataset. LightGBM is a framework for gradient boosting that incorporates multiple tree-based learning algorithms.

For this section, we will follow a typical best-practice approach using Azure Machine Learning and perform the following steps:

  1. Register the dataset in Azure.
  2. Create a remote compute cluster.
  3. Implement a configurable training script.
  4. Run the training script on the compute cluster.
  5. Log and collect the dataset, parameters, and performance.
  6. Register the trained model.

Before we start with this exciting approach, we'll take a quick look at why we chose LightGBM as a tool for training bagged and boosted tree...

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