Book Image

Hands-On Machine Learning with Azure

By : Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak, Murphy
Book Image

Hands-On Machine Learning with Azure

By: Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak, Murphy

Overview of this book

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.
Table of Contents (14 chapters)

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...