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Hands-On Machine Learning with Azure

You're reading from   Hands-On Machine Learning with Azure Build powerful models with cognitive machine learning and artificial intelligence

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789131956
Length 340 pages
Edition 1st Edition
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Authors (6):
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Jen Stirrup Jen Stirrup
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Jen Stirrup
Ryan Murphy Ryan Murphy
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Ryan Murphy
Anindita Basak Anindita Basak
Author Profile Icon Anindita Basak
Anindita Basak
Thomas K Abraham Thomas K Abraham
Author Profile Icon Thomas K Abraham
Thomas K Abraham
Parashar Shah Parashar Shah
Author Profile Icon Parashar Shah
Parashar Shah
Lauri Lehman Lauri Lehman
Author Profile Icon Lauri Lehman
Lauri Lehman
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Table of Contents (14) Chapters Close

Preface 1. AI Cloud Foundations FREE CHAPTER 2. Data Science Process 3. Cognitive Services 4. Bot Framework 5. Azure Machine Learning Studio 6. Scalable Computing for Data Science 7. Machine Learning Server 8. HDInsight 9. Machine Learning with Spark 10. Building Deep Learning Solutions 11. Integration with Other Azure Services 12. End-to-End Machine Learning 13. Other Books You May Enjoy

Azure Data Lake Analytics

Azure Data Lake (ADL) is Microsoft's storage and analytics service for big data. It is capable of storing data on a petabyte scale and making efficient queries on the stored data. The storage and the analytics services are separate in Azure and the ADL service actually consists of two different products: Azure Data Lake Storage (ADLS) and Azure Data Lake Analytics (ADLA). In this section, we will focus on ADLA, but we will also touch on ADLS where appropriate.

Data Lake Storage is a file-based storage, with files organized into directories. This type of storage is called schemaless, since there are no constraints on what type of data can be stored in the Data Lake. Directories can contain text files and images, and the data type is specified only when the data is read out from the Data Lake. This is particularly useful in big data scenarios where...

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