Machine Learning with Microsoft Technologies: Selecting the Right Architecture and Tools for Your Project - Softcover

Etaati, Leila

Softcover

ISBN 10: 1484236572 ISBN 13: 9781484236574

Publisher: Apress, 2019

Synopsis

Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more.

The ability to analyze massive amounts of real-time data and predict future behavior of an organization is critical to its long-term success. Data science, and more specifically machine learning (ML), is today’s game changer and should be a key building block in every company’s strategy. Managing a machine learning process from business understanding, data acquisition and cleaning, modeling, and deployment in each tool is a valuable skill set.

Machine Learning with Microsoft Technologies is a demo-driven book that explains how to do machine learning with Microsoft technologies. You will gain valuable insight into designing the best architecture for development, sharing, and deploying a machine learning solution. This book simplifies the process of choosing the right architecture and tools for doing machine learning based on your specific infrastructure needs and requirements.

Detailed content is provided on the main algorithms for supervised and unsupervised machine learning and examples show ML practices using both R and Python languages, the main languages inside Microsoft technologies. 

What You'll Learn

• Choose the right Microsoft product for your machine learning solution
• Create and manage Microsoft’s tool environments for development, testing, and production of a machine learning project
• Implement and deploy supervised and unsupervised learning in Microsoft products
• Set up Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, and HD Insight to perform machine learning
• Set up a data science virtual machine and test-drive installed tools, such as Azure ML Workbench, Azure ML Server Developer, Anaconda Python, Jupyter Notebook, Power BI Desktop, Cognitive Services, machine learning and data analytics tools, and more
• Architect a machine learning solution factoring in all aspects of self service, enterprise, deployment, and sharing
  

Who This Book Is For

Data scientists, data analysts, developers, architects, and managers who want to leverage machine learning in their products, organization, and services, and make educated, cost-saving decisions about their ML architecture and tool set.

 

About this title

About the Author

Leila Etaati, PhD, is a Microsoft artificial intelligence and data platform MVP, speaker, trainer, and founding consultant with RADACAD where she trains and strategically advises some of today’s largest global enterprises. Renowned in the field of AI and BI, she presents at many Microsoft events, including Ignite, Microsoft Data Insights Summit, PASS, and more. Leila is passionate about teaching others and resolving complex business solutions through the vast capabilities of machine learning and BI. She blogs and is author of Power BI and R through RADACAD.

From the Back Cover

Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more.

The ability to analyze massive amounts of real-time data and predict future behavior of an organization is critical to its long-term success. Data science, and more specifically machine learning (ML), is today’s game changer and should be a key building block in every company’s strategy. Managing a machine learning process from business understanding, data acquisition and cleaning, modeling, and deployment in each tool is a valuable skill set.

Machine Learning with Microsoft Technologies is a demo-driven book that explains how to do machine learning with Microsoft technologies. You will gain valuable insight into designing the best architecture for development, sharing, and deploying a machine learning solution. This book simplifies the process of choosing the right architecture and tools for doing machine learning based on your specific infrastructure needs and requirements.

Detailed content is provided on the main algorithms for supervised and unsupervised machine learning and examples show ML practices using both R and Python languages, the main languages inside Microsoft technologies. 

What You'll Learn:

• Choose the right Microsoft product for your machine learning solution
• Create and manage Microsoft’s tool environments for development, testing, and production of a machine learning project
• Implement and deploy supervised and unsupervised learning in Microsoft products
• Set up Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, and HD Insight to perform machine learning
• Set up a data science virtual machine and test-drive installed tools, such as Azure ML Workbench, Azure ML ServerDeveloper, Anaconda Python, Jupyter Notebook, Power BI Desktop, Cognitive Services, machine learning and data analytics tools, and more
• Architect a machine learning solution factoring in all aspects of self service, enterprise, deployment, and sharing
  

This book is for data scientists, data analysts, developers, architects, and managers who want to leverage machine learning in their products, organization, and services, and make educated, cost-saving decisions about their ML architecture and tool set.

​Leila Etaati, PhD, is a Microsoft artificial intelligence and data platform MVP, speaker, trainer, and founding consultant with RADACAD where she trains and strategically advises some of today’s largest global enterprises. Renowned in the field of AI and BI, she presents at many Microsoft events, including Ignite, Microsoft Data Insights Summit, PASS, and more. Leila is passionate about teaching others and resolving complex business solutions through the vast capabilities of machine learning and BI. She blogs and is author of Power BI and R through RADACAD.

Edition details

• Publisher: Apress
• Publication date: 2019
• Language: English
• ISBN 10: 1484236572
• ISBN 13: 9781484236574
• Binding: Paperback
• Edition number: 1
• Number of pages: 380