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Hands-On Machine Learning with Azure
Hands-On Machine Learning with Azure

Hands-On Machine Learning with Azure: Build powerful models with cognitive machine learning and artificial intelligence

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Profile Icon Parashar Shah Profile Icon Abraham Profile Icon Murphy Profile Icon Lauri Lehman Profile Icon Jen Stirrup Profile Icon Anindita Basak +2 more Show less
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NZ$14.99 NZ$57.99
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.7 (3 Ratings)
eBook Oct 2018 340 pages 1st Edition
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NZ$14.99 NZ$57.99
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Arrow left icon
Profile Icon Parashar Shah Profile Icon Abraham Profile Icon Murphy Profile Icon Lauri Lehman Profile Icon Jen Stirrup Profile Icon Anindita Basak +2 more Show less
Arrow right icon
NZ$14.99 NZ$57.99
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.7 (3 Ratings)
eBook Oct 2018 340 pages 1st Edition
eBook
NZ$14.99 NZ$57.99
Paperback
NZ$71.99
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Free Trial
eBook
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Paperback
NZ$71.99
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Hands-On Machine Learning with Azure

Data Science Process

Over the past decade, organizations have seen a rapid growth in data. Harnessing insight from that data is crucial to the growth and sustenance of these organizations. Yet, groups chartered with extracting value from data fail for various reasons. In this chapter, we will cover how organizations can avoid the potential pitfalls of data science.

There is a larger discussion about the quality and governance of data, which we will not be covering here. Experienced data scientists recognize the challenges with data and account for them in their processes. In general, some of these challenges include the following:

  • Poor data quality and consistency
  • Silos of data driven by individual business teams
  • Technologies that are hard to integrate with other data sources
  • The inability to deal with the Vs of big data: volume, velocity, variety, and veracity

In some cases...

TDSP stages

The Team Data Science Process (TDSP) is a methodology created by Microsoft to guide the full life cycle of data science projects in organizations. It is not meant to be a complete solution, but simply a framework by which teams can add structure to their processes and achieve the full business value of their analytics.

Besides TDSP, the other prevalent methodology that organizations have been adopting is called CRISP-DM (short for Cross-Industry Standard Process for Data Mining). This methodology has been around since the mid-1990s. There were several attempts to update it in the 2000s, but they were abandoned. The primary focus of CRISP-DM was data mining, but its principles can be extended to data science as well. The major steps listed in CRISP-DM are as follows: business understanding, data understanding, data preparation, modeling, evaluation, and deployment....

Tools for TDSP

Microsoft has released a set of tools that make it easier for organizations to follow the TDSP process. One of those tools is the IDEAR utility released for CRAN-R, Microsoft R, and Python. Another tool is the Automated Modeling and Reporting (AMAR) utility. In this section, we will look into how we can leverage these tools in the TDSP process.

IDEAR tool for R

Summary

In conclusion, we have introduced you to the TDSP in this chapter and covered each of the different steps that are involved in detail. This process is meant to augment other existing processes rather than replace them. We also looked at various TDSP utilities that Microsoft has provided that make it easier to build some structure into the data science life cycle. In the next few chapters, we will look at each of the options available within Azure to build AI solutions for your business needs.

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Key benefits

  • Learn advanced concepts in Azure ML and the Cortana Intelligence Suite architecture
  • Explore ML Server using SQL Server and HDInsight capabilities
  • Implement various tools in Azure to build and deploy machine learning models

Description

Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way. The book begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft’s Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This book lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The book then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding chapters, you’ll integrate patterns with other non-AI services in Azure. By the end of this book, you will be fully equipped to implement smart cognitive actions in your models.

Who is this book for?

If you are a data scientist or developer familiar with Azure ML and cognitive services and want to create smart models and make sense of data in the cloud, this book is for you. You’ll also find this book useful if you want to bring powerful machine learning services into your cloud applications. Some experience with data manipulation and processing, using languages like SQL, Python, and R, will aid in understanding the concepts covered in this book

What you will learn

  • Discover the benefits of leveraging the cloud for ML and AI
  • Use Cognitive Services APIs to build intelligent bots
  • Build a model using canned algorithms from Microsoft and deploy it as a web service
  • Deploy virtual machines in AI development scenarios
  • Apply R, Python, SQL Server, and Spark in Azure
  • Build and deploy deep learning solutions with CNTK, MMLSpark, and TensorFlow
  • Implement model retraining in IoT, Streaming, and Blockchain solutions
  • Explore best practices for integrating ML and AI functions with ADLA and logic apps

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 31, 2018
Length: 340 pages
Edition : 1st
Language : English
ISBN-13 : 9781789130270
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Publication date : Oct 31, 2018
Length: 340 pages
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Language : English
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Table of Contents

13 Chapters
AI Cloud Foundations Chevron down icon Chevron up icon
Data Science Process Chevron down icon Chevron up icon
Cognitive Services Chevron down icon Chevron up icon
Bot Framework Chevron down icon Chevron up icon
Azure Machine Learning Studio Chevron down icon Chevron up icon
Scalable Computing for Data Science Chevron down icon Chevron up icon
Machine Learning Server Chevron down icon Chevron up icon
HDInsight Chevron down icon Chevron up icon
Machine Learning with Spark Chevron down icon Chevron up icon
Building Deep Learning Solutions Chevron down icon Chevron up icon
Integration with Other Azure Services Chevron down icon Chevron up icon
End-to-End Machine Learning Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.7
(3 Ratings)
5 star 33.3%
4 star 0%
3 star 0%
2 star 33.3%
1 star 33.3%
Michael L. Friscia May 25, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I attended Live AI in Orlando in December 2018, shortly after this book came out, and the book was a great refresher on doing many things I had forgotten how to do. The only trouble is that Azure has changed their interface a little and added many new features. So some of the screen shots in this book are outdated but the information is accurate. They also make references to tutorials, they still exist in the Azure GitHub but not in the same way in Azure portal as the book references.The problem with Machine Learning is that there are too many different aspects to cover. I think this book does a great job at touching on most of them. But for me, I wanted something to help navigate the Azure tools to give me a kick in the right direction. I know Python, I've worked with R and I've done a handful of tensorflow stuff on Linux docker images. But my work is entirely in Azure.If you are new to Azure and Machine Learning, I think this book is good. It summarizes a lot of what you can find in the Microsoft tutorials online for getting started with Azure ML studio and puts it into easy to follow steps. But be forewarned that Azure changes often, the concepts in this book remain solid but in another year, most of the steps will likely be much different. The change between December and now is pretty big in that Azure has added numerous new features to their AI/ML offerings. Having worked in Azure for a couple of years, I know in 6 months it will be even more different which will probably make this book obsolete by the summer of 2020 except for advanced Azure users that can translate this book to the newer Azure interface.I do recommend this book but keep i mind, it is exactly what the title says it is. It is not a book to teach how to do machine learning, it is a hands on book using microsoft experiments published in GitHub for use on the Azure platform that will help you understand how to use Azure tools for machine learning.
Amazon Verified review Amazon
Colin Dec 12, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
For the price I was expecting something far more in-depth. It's a big book but most of it is just screenshots, and much of the rest is just blurb/ML hype. Terminology isn't explained and the authors skim over most detail - eg we are informed that R code can be embedded into ML studio to enable you to create bespoke ML algorithms, but there is no detail on how to actually go about doing this. Similarly there is a lack of detail on the other available functions in ML studio. Also, whilst there is a whole chapter on the data science lifecycle process, there is little detail on the application of this - ie what I, as a data scientist who is new to Azure, need to do to get from raw data in a lake to a fully deployed embedded ML solution.
Amazon Verified review Amazon
Md Abu S. Chowdhury Apr 26, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Very poor Graphics Contents.. Unable to read many image contents
Amazon Verified review Amazon
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