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Data Science Solutions on Azure: Tools and Techniques Using Databricks and MLOps 1st ed. [Pehme köide]

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  • Formaat: Paperback / softback, 285 pages, kõrgus x laius: 235x155 mm, kaal: 462 g, 186 Illustrations, black and white; XIII, 285 p. 186 illus., 1 Paperback / softback
  • Ilmumisaeg: 19-Dec-2020
  • Kirjastus: APress
  • ISBN-10: 1484264045
  • ISBN-13: 9781484264041
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Data Science Solutions on Azure: Tools and Techniques Using Databricks and MLOps 1st ed.Suurem pilt
  • Formaat: Paperback / softback, 285 pages, kõrgus x laius: 235x155 mm, kaal: 462 g, 186 Illustrations, black and white; XIII, 285 p. 186 illus., 1 Paperback / softback
  • Ilmumisaeg: 19-Dec-2020
  • Kirjastus: APress
  • ISBN-10: 1484264045
  • ISBN-13: 9781484264041
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781484264041.html
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. 


About the Authors ix
About the Technical Reviewers xi
Acknowledgments xiii
Chapter 1 Data Science in the Modern Enterprise 1(14)
Mindset of the modern enterprise
2(2)
Commercial entities
2(1)
Government entities
3(1)
Consumer and personal
3(1)
Ethics in data science
4(4)
Science fiction and reality - a social convergence
4(4)
Azure Machine Learning
8(2)
Azure Machine Learning
10(1)
Machine Learning Studio (classic)
10(1)
Azure Databricks (Chapters 6 and 7)
10(3)
Use cases for Azure Databricks
11(2)
Summary
13(2)
Chapter 2 Statistical Techniques and Concepts in Data Science 15(50)
The fundamentals
16(1)
Inputs and outputs
16(1)
Lab setup
17(9)
Hands-on exercise: Understanding inputs, outputs, and simple modeling
18(4)
Prediction and inference
22(4)
Machine learning
26(35)
Deriving f through machine learning
26(6)
Types of machine learning
32(1)
Supervised learning
32(20)
Unsupervised learning
52(9)
Causation and correlation
61(2)
Use case: Problem diagnosis
62(1)
Experiment design
62(1)
Summary
63(2)
Chapter 3 Data Preparation and Data Engineering Basics 65(52)
What is data preparation?
66(1)
The challenge of data preparation
67(1)
Modern data pipeline
68(2)
Data ingestion - Sources
69(1)
Transformation
70(1)
Azure Blob Storage
70(12)
Hands-on: Deploying Azure Blob Storage
73(4)
Hands-on: Using Azure Blob Storage
77(4)
Next steps: Azure Blob Storage
81(1)
Azure Data Lake Store (ADLS)
82(1)
Data orchestration
83(32)
Azure Data Factory (ADF)
84(31)
Summary
115(2)
Chapter 4 Introduction to Azure Machine Learning 117(32)
Machine learning primer
117(3)
Why machine learning on cloud?
118(2)
Microsoft Azure Machine Learning
120(5)
Azure Machine Learning workspace
125(1)
Azure Machine Learning workflow
125(23)
Azure Machine Learning Datastores and Datasets
127(3)
Azure Machine Learning compute and development environments
130(1)
Azure Machine Learning compute instance
131(2)
Azure Machine Learning compute target
133(3)
Azure Machine Learning inference clusters
136(2)
Azure Machine Learning attached compute
138(1)
Azure Machine Learning experiments
139(2)
Azure Machine Learning pipelines
141(2)
Azure Machine Learning designer pipelines
143(3)
Azure Machine Learning models and endpoints
146(2)
Summary
148(1)
Chapter 5 Hands-on with Azure Machine Learning 149(52)
Lab setup
149(4)
Getting started with JupyterLab
153(2)
Prerequisite setup
155(9)
Training on remote cluster
164(10)
Deploying your model as web service
174(7)
Automated Machine Learning (AutoML)
181(1)
AutoML studio web
182(10)
AutoML Python SDK
192(7)
Summary
199(2)
Chapter 6 Apache Spark, Big Data, and Azure Databricks 201(24)
Big Data
202(5)
Example of Big Data based on size
203(1)
Example of Big Data based on number of rows
203(2)
Example of Big Data based on velocity
205(1)
Other challenges of Big Data - data trends
206(1)
Compute
207(1)
Apache Spark and Hadoop
208(2)
Apache Spark
210(2)
Databricks
212(1)
Azure Databricks
213(10)
Azure Databricks for data engineers
216(4)
Azure Databricks for data scientists
220(3)
Summary
223(2)
Chapter 7 Hands-on with Azure Databricks 225(34)
Deploying Azure Databricks
225(2)
Exploring Azure Databricks
227(30)
Create Spark compute cluster
228(4)
Customizing clusters
232(3)
Connecting to clusters
235(8)
Databricks notebooks
243(14)
Summary
257(2)
Chapter 8 Machine Learning Operations 259(22)
Introducing MLOps
259(20)
Capabilities of MLOps
260(19)
Summary
279(2)
Index 281
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 masters 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/