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|a 9781484254707
|9 978-1-4842-5470-7
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|a 10.1007/978-1-4842-5470-7
|2 doi
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|a Stackowiak, Robert.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Azure Internet of Things Revealed
|h [electronic resource] :
|b Architecture and Fundamentals /
|c by Robert Stackowiak.
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| 250 |
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|a 1st ed. 2019.
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| 264 |
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|a Berkeley, CA :
|b Apress :
|b Imprint: Apress,
|c 2019.
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| 300 |
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|a XV, 205 p. 96 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a 1. Modern IoT Architecture Patterns -- 2. Azure IoT Solutions Overview -- 3. IoT Edge Devices and Microsoft -- 4. Azure IoT Hub -- 5. Analyzing and Visualizing Data in Azure -- 6. IoT Central & Solution Accelerators -- 7. Infrastructure Integration -- 8. Developing a Plan for Success -- 9. Appendix: Published Sources.
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| 520 |
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|a Design, build, and justify an optimal Microsoft IoT footprint to meet your project needs. This book describes common Internet of Things components and architecture and then focuses on Microsoft's Azure components relevant in deploying these solutions. Microsoft-specific topics addressed include: deploying edge devices and pushing intelligence to the edge; connecting IoT devices to Azure and landing data there, applying Azure Machine Learning, analytics, and Cognitive Services; roles for Microsoft solution accelerators and managed solutions; and integration of the Azure footprint with legacy infrastructure. The book concludes with a discussion of best practices in defining and developing solutions and creating a plan for success. You will: Design the right IoT architecture to deliver solutions for a variety of project needs Connect IoT devices to Azure for data collection and delivery of services Use Azure Machine Learning and Cognitive Services to deliver intelligence in cloud-based solutions and at the edge Understand the benefits and tradeoffs of Microsoft's solution accelerators and managed solutions Investigate new use cases that are described and apply best practices in deployment strategies Integrate cutting-edge Azure deployments with existing legacy data sources.
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|a Microsoft software.
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|a Microsoft .NET Framework.
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|a Machine learning.
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|a Microsoft and .NET.
|0 http://scigraph.springernature.com/things/product-market-codes/I29030
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|a Machine Learning.
|0 http://scigraph.springernature.com/things/product-market-codes/I21010
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9781484254691
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|i Printed edition:
|z 9781484254714
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|u https://doi.org/10.1007/978-1-4842-5470-7
|z Full Text via HEAL-Link
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|a ZDB-2-CWD
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| 950 |
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|a Professional and Applied Computing (Springer-12059)
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