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Azure Machine Learning Engineering

You're reading from   Azure Machine Learning Engineering Deploy, fine-tune, and optimize ML models using Microsoft Azure

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Product type Paperback
Published in Jan 2023
Publisher Packt
ISBN-13 9781803239309
Length 362 pages
Edition 1st Edition
Tools
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Authors (4):
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Balamurugan Balakreshnan Balamurugan Balakreshnan
Author Profile Icon Balamurugan Balakreshnan
Balamurugan Balakreshnan
Dennis Michael Sawyers Dennis Michael Sawyers
Author Profile Icon Dennis Michael Sawyers
Dennis Michael Sawyers
Sina Fakhraee Ph.D Sina Fakhraee Ph.D
Author Profile Icon Sina Fakhraee Ph.D
Sina Fakhraee Ph.D
Megan Masanz Megan Masanz
Author Profile Icon Megan Masanz
Megan Masanz
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Training and Tuning Models with the Azure Machine Learning Service
2. Chapter 1: Introducing the Azure Machine Learning Service FREE CHAPTER 3. Chapter 2: Working with Data in AMLS 4. Chapter 3: Training Machine Learning Models in AMLS 5. Chapter 4: Tuning Your Models with AMLS 6. Chapter 5: Azure Automated Machine Learning 7. Part 2: Deploying and Explaining Models in AMLS
8. Chapter 6: Deploying ML Models for Real-Time Inferencing 9. Chapter 7: Deploying ML Models for Batch Scoring 10. Chapter 8: Responsible AI 11. Chapter 9: Productionizing Your Workload with MLOps 12. Part 3: Productionizing Your Workload with MLOps
13. Chapter 10: Using Deep Learning in Azure Machine Learning 14. Chapter 11: Using Distributed Training in AMLS 15. Index 16. Other Books You May Enjoy

Setting up a sweep job for random sampling

As we saw with setting up a command for grid sampling, a command for random sampling is simply a job command with the hyperparameters included in the command. One difference between the grid command and the random command is that the hyperparameters can be continuous in a random sampling sweep job.

Here’s the code for the job command for random sampling:

Figure 4.20 – Sweep job command with hyperparameters for random sampling

Figure 4.20 – Sweep job command with hyperparameters for random sampling

As shown in Figure 4.20 in line 25, the value of C is defined to follow a uniform distribution from 0.01 to 10.0, making this a continuous hyperparameter across the search space.

Just as with the grid sampling sweep job, we set parameters for our sweep but specify them using a random sampling algorithm as shown here:

Figure 4.21 – Calling the sweep method during random sampling

Figure 4.21 – Calling the sweep method during random sampling

Given the parameters for the sweep job and the limits have...

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