Hardcover. Apply cutting-edge AI techniques to your Dynamics 365 environment to create new solutions to old business problems In Machine Learning with Dynamics 365 and Power Platform: The Ultimate Guide to Apply Predictive Analytics, an accomplished team of digital and data analytics experts delivers a practical and comprehensive discussion of how to integrate AI Builder with Dataverse and Dynamics 365 to create real-world business solutions. It also walks you through how to build powerful machine learning models using Azure Data Lake, Databricks, Azure Synapse Analytics. The book is filled with clear explanations, visualizations, and working examples that get you up and running in your development of supervised, unsupervised, and reinforcement learning techniques using Microsoft machine learning tools and technologies. These strategies will transform your business verticals, reducing costs and manual processes in finance and operations, retail, telecommunications, and manufacturing industries. The authors demonstrate: What machine learning is all about and how it can be applied to your organization's Dynamics 365 and Power Platform ProjectsThe creation and management of environments for development, testing, and production of a machine learning projectHow adopting machine learning techniques will redefine the future of your ERP/CRM system Perfect for Technical Consultants, software developers, and solution architects, Machine Learning with Dynamics 365 and Power Platform is also an indispensable guide for Chief Technology Officers seeking an intuitive resource for how to implement machine learning in modern business applications to solve real-world problems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

N� de ref. del art�culo 9781119771296

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Detalles bibliogr�ficos

T�tulo: Machine Learning with Dynamics 365 and Power Platform (Hardcover)
Editor: John Wiley & Sons Inc, New York
Fecha de publicaci�n: 2022
Idioma: Ingl�s
ISBN 10: 1119771293
ISBN 13: 9781119771296
Encuadernaci�n: Hardcover
Estado: new

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Sinopsis

Apply cutting-edge AI techniques to your Dynamics 365 environment to create new solutions to old business problems

In Machine Learning with Dynamics 365 and Power Platform: The Ultimate Guide to Apply Predictive Analytics, an accomplished team of digital and data analytics experts delivers a practical and comprehensive discussion of how to integrate AI Builder with Dataverse and Dynamics 365 to create real-world business solutions. It also walks you through how to build powerful machine learning models using Azure Data Lake, Databricks, Azure Synapse Analytics.

The book is filled with clear explanations, visualizations, and working examples that get you up and running in your development of supervised, unsupervised, and reinforcement learning techniques using Microsoft machine learning tools and technologies. These strategies will transform your business verticals, reducing costs and manual processes in finance and operations, retail, telecommunications, and manufacturing industries.

The authors demonstrate:

What machine learning is all about and how it can be applied to your organization's Dynamics 365 and Power Platform Projects
The creation and management of environments for development, testing, and production of a machine learning project
How adopting machine learning techniques will redefine the future of your ERP/CRM system

Perfect for Technical Consultants, software developers, and solution architects, Machine Learning with Dynamics 365 and Power Platform is also an indispensable guide for Chief Technology Officers seeking an intuitive resource for how to implement machine learning in modern business applications to solve real-world problems.

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AURELIEN CLERE is a Microsoft MVP, Global Solution Architect with 10 years of experience in the Dynamics world (ERP, CRM). He is a speaker and organizes weekly webinars.

VINNIE BANSAL is an independent Dynamics 365 Business Advisor. He liaises between business and IT teams and serves as technical advisor to clients in assigned subject areas.

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