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Data Science Solutions on Azure: Tools and Techniques Using Databricks and MLOps de Julian Soh

Data Science Solutions on Azure: Tools and Techniques Using Databricks and MLOps

Autor Julian Soh, Priyanshi Singh
en Limba Engleză Paperback – 19 dec 2020
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. 

What You'll Learn
  • 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
Who This Book Is For

Data Scientists looking to deploy end-to-end solutions on Azure with latest tools and techniques. 


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Specificații

ISBN-13: 9781484264041
ISBN-10: 1484264045
Pagini: 285
Ilustrații: XIII, 285 p. 186 illus.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.42 kg
Ediția:1st ed.
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States

Cuprins

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.

Notă biografică

Julian Soh is a cloud solutions architect with Microsoft, focusing in the areas of artificial intelligence, cognitive services, and advanced analytics. Prior to his current role, Julian worked extensively in major public cloud initiatives, such as SaaS (Microsoft Office 365), IaaS/PaaS (Microsoft Azure), and hybrid private-public cloud implementations.

Priyanshi Singh is a data scientist by training and a data enthusiast by nature specializing in machine learning techniques applied to predictive analytics, computer vision and natural language processing. She holds a master’s degree in Data Science from New York University and is currently a Cloud Solution Architect at Microsoft helping the public sector to transform citizen services with Artificial Intelligence. She also leads a meetup community based out of New York to help educate public sector employees via hands on labs and discussions. Apart from her passion for learning new technologies and innovating with AI, she is a sports enthusiast, a great badminton player and enjoys playing Billiards. Find her on LinkedIn at https://www.linkedin.com/in/priyanshi-singh5/

Textul de pe ultima copertă

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

Caracteristici

Holistic coverage of Azure data science capabilities Covers Azure Cognitive Services API for AI developers Consists of Case Studies for each operation discussed