Book Image

Hands-On Machine Learning with Azure

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

Hands-On Machine Learning with Azure

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

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)

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....