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Learn how to build intelligent data applications with Amazon Web Services (AWS) : understanding and using AWS products and services, AWS Data Pipeline, Kinesis Analytics, RDS and Redshift databases, and Amazon Machine Learning

Author: John Hearty
Publisher: [Place of publication not identified] : O'Reilly, [2017]
Edition/Format:   eVideo : Clipart/images/graphics : English
Summary:
"This course shows you how to use a range of AWS services to create intelligent end-to-end applications that incorporate ingestion, storage, preprocessing, machine learning (ML), and connectivity to an application client or server. The course is designed for data scientists looking for clear instruction on how to deploy locally developed ML applications to the AWS platform, and for developers who want to add machine  Read more...
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Details

Material Type: Clipart/images/graphics, Internet resource, Videorecording
Document Type: Internet Resource, Computer File, Visual material
All Authors / Contributors: John Hearty
OCLC Number: 1000390998
Notes: Title from title screen (viewed August 10, 2017).
Date of publication from resource description page.
Performer(s): Presenter, John Hearty.
Description: 1 online resource (1 streaming video file (3 hr., 23 min., 42 sec.)) : digital, sound, color
Responsibility: with John Hearty.

Abstract:

"This course shows you how to use a range of AWS services to create intelligent end-to-end applications that incorporate ingestion, storage, preprocessing, machine learning (ML), and connectivity to an application client or server. The course is designed for data scientists looking for clear instruction on how to deploy locally developed ML applications to the AWS platform, and for developers who want to add machine learning capabilities to their applications using AWS services. Prerequisites include: Basic awareness of Amazon Simple Storage Service (S3), Elastic Compute Cloud (EC2), and Amazon Elastic MapReduce; as well as some knowledge of ML concepts like classification and regression analysis, model types, training and performance measures; and a general understanding of Python."--Resource description page.

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Primary Entity

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