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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

Creating a data asset using the Python SDK

In this section, we will show you how to create a data asset using the Python SDK. As mentioned in the previous section, you can create data from datastores, local files, and public URLs. The Python script to create a data asset from a local file (for example, titanic.csv) is shown in Figure 2.19.

Please note that in the following code snippet, type = AssetTypes.mltable abstracts the schema definition for the tabular data, making it easier to share datasets:

Figure 2.19 – Creating a data asset via the Python SDK

Figure 2.19 – Creating a data asset via the Python SDK

Inside the my_data folder, there are two files:

  • The actual data file, which in this case is titanic.csv
  • The mltable file, which is a YAML file specifying the data’s schema so that the mltable engine can use it in order to materialize the data into an in-memory object such as pandas or DASK

Figure 2.20 shows the mltable YAML file for this example:

Figure 2.20 – The mltable YAML file for creating an mltable data asset...
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