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Soh, Julian.

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Data science solutions on Azure = tools and techniques using Databricks and MLOps /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science solutions on Azure/ by Julian Soh, Priyanshi Singh.
Reminder of title:	tools and techniques using Databricks and MLOps /
Author:	Soh, Julian.
other author:	Singh, Priyanshi.
Published:	Berkeley, CA :Apress : : 2020.,
Description:	xiii, 285 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Data Science in the Modern Enterprise -- Chapter 2: Statistical Techniques and Concepts in Data Science -- Chapter 3: Data Preparation and Data Engineering Basics -- Chapter 4: Introduction to Azure Machine Learning -- Chapter 5: Hands on with Azure Machine Learning -- Chapter 6: Apache Spark, Big Data, and Azure Databricks -- Chapter 7: Hands-on with Azure Databricks -- Chapter 8: Machine Learning Operations.
Contained By:	Springer Nature eBook
Subject:	Microsoft Azure (Computing platform) -
Online resource:	https://doi.org/10.1007/978-1-4842-6405-8
ISBN:	9781484264058

Data science solutions on Azure = tools and techniques using Databricks and MLOps /
Soh, Julian.

Data science solutions on Azuretools and techniques using Databricks and MLOps /[electronic resource] :by Julian Soh, Priyanshi Singh. - Berkeley, CA :Apress :2020. - xiii, 285 p. :ill., digital ;24 cm.

Chapter 1: Data Science in the Modern Enterprise -- Chapter 2: Statistical Techniques and Concepts in Data Science -- Chapter 3: Data Preparation and Data Engineering Basics -- Chapter 4: Introduction to Azure Machine Learning -- Chapter 5: Hands on with Azure Machine Learning -- Chapter 6: Apache Spark, Big Data, and Azure Databricks -- Chapter 7: Hands-on with Azure Databricks -- Chapter 8: Machine Learning Operations.

Understand and learn the skills needed to use modern tools in Microsoft Azure. This book discusses how to practically apply these tools in the industry, and help drive the transformation of organizations into a knowledge and data-driven entity. It provides an end-to-end understanding of data science life cycle and the techniques to efficiently productionize workloads. The book starts with an introduction to data science and discusses the statistical techniques data scientists should know. You'll then move on to machine learning in Azure where you will review the basics of data preparation and engineering, along with Azure ML service and automated machine learning. You'll also explore Azure Databricks and learn how to deploy, create and manage the same. In the final chapters you'll go through machine learning operations in Azure followed by the practical implementation of artificial intelligence through machine learning. Data Science Solutions on Azure will reveal how the different Azure services work together using real life scenarios and how-to-build solutions in a single comprehensive cloud ecosystem. You will: Understand big data analytics with Spark in Azure Databricks Integrate with Azure services like Azure Machine Learning and Azure Synaps Deploy, publish and monitor your data science workloads with MLOps Review data abstraction, model management and versioning with GitHub.

ISBN: 9781484264058

Standard No.: 10.1007/978-1-4842-6405-8doiSubjects--Topical Terms:

3201298
Microsoft Azure (Computing platform)

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Data science solutions on Azure = tools and techniques using Databricks and MLOps /
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505 0 $a Chapter 1: Data Science in the Modern Enterprise -- Chapter 2: Statistical Techniques and Concepts in Data Science -- Chapter 3: Data Preparation and Data Engineering Basics -- Chapter 4: Introduction to Azure Machine Learning -- Chapter 5: Hands on with Azure Machine Learning -- Chapter 6: Apache Spark, Big Data, and Azure Databricks -- Chapter 7: Hands-on with Azure Databricks -- Chapter 8: Machine Learning Operations.
520 $a Understand and learn the skills needed to use modern tools in Microsoft Azure. This book discusses how to practically apply these tools in the industry, and help drive the transformation of organizations into a knowledge and data-driven entity. It provides an end-to-end understanding of data science life cycle and the techniques to efficiently productionize workloads. The book starts with an introduction to data science and discusses the statistical techniques data scientists should know. You'll then move on to machine learning in Azure where you will review the basics of data preparation and engineering, along with Azure ML service and automated machine learning. You'll also explore Azure Databricks and learn how to deploy, create and manage the same. In the final chapters you'll go through machine learning operations in Azure followed by the practical implementation of artificial intelligence through machine learning. Data Science Solutions on Azure will reveal how the different Azure services work together using real life scenarios and how-to-build solutions in a single comprehensive cloud ecosystem. You will: Understand big data analytics with Spark in Azure Databricks Integrate with Azure services like Azure Machine Learning and Azure Synaps Deploy, publish and monitor your data science workloads with MLOps Review data abstraction, model management and versioning with GitHub.
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