Database Reference
In-Depth Information
Introducing MovieStream
To better illustrate the design of our architecture, we will introduce a practical scenario.
Let's assume that we have just been appointed to head the data science team of
MovieStream, a fictitious Internet business that streams movies and television shows to its
users.
MovieStream is growing rapidly, adding both users and titles to its catalogue. The current
MovieStream system is outlined in the following diagram:
MovieStream's current architecture
As we can see in the preceding diagram, currently, MovieStream's content editorial team is
responsible for deciding which movies and shows are promoted and shown on the various
parts of the site. They are also responsible for creating the content for MovieStream's bulk
marketing campaigns, which include e-mail and other direct marketing channels. Currently,
MovieStream collects basic data on what titles are viewed by users on an aggregate basis
and has access to some demographic data collected from users when they sign up to the
service. In addition, they have access to some basic metadata about the titles in their cata-
logue.
The MovieStream team is stretched thin due to their rapid growth, and they can't keep up
with the number of new releases and the growing activity of their users. The CEO of
MovieStream has heard a lot about big data, machine learning, and artificial intelligence,
and would like us to build a machine learning system for MovieStream that can handle
many of the functions currently handled by the content team in an automated manner.
