25949117

pay-by-the-minute general availability of massive computing power literally at your fingertips, you can
easily see how predictive tools like Azure Machine Learning are becoming crucial to the success of
almost any government, industry, business, or enterprise today.
The reality is that the use of predictive analytics is rapidly encompassing many aspects of our daily
lives to help us make better and more informed decisions. At some point in our very near future, we
might even find that the notion of “guessing” at any major decision will become passé.
With Azure Machine Learning tools and services, the rate at which new predictive models can be
generated and publicly exposed on the Internet has now approached lightning speed. Using Azure
Machine Learning, it is now possible to create, test, and deploy a new predictive analytics service in only
a matter of hours. Compare that deployment timeline with the days, weeks, and even months that it
might take with other commercially available solutions on the market today.
Certainly one of the keys to success with predictive analytics is the ability to “fail fast”. A fast fail
provides immediate feedback and creates immediate fine-tuning opportunities for a given predictive
model. The Azure Machine Learning workflow seeks to optimize this process in a very agile and iterative
way, so that today’s data scientist can quickly advance prediction solutions and start to evaluate the
results that will lead to potentially significant effects on the business.
Figure 2-6 summarizes the three basic high-level steps that are required to create, test, and deploy a
new Azure Machine Learning prediction model based on the concept of supervised learning.
32