Database Reference
In-Depth Information
Business use cases for a machine learning
system
Perhaps the first question we should answer is, "Why use machine learning at all?" Why
doesn't MovieStream simply continue with human-driven decisions? There are many reas-
ons to use machine learning (and certainly some reasons not to), but the most important
ones are mentioned here:
• The scale of data involved means that full human involvement quickly becomes in-
feasible as MovieStream grows
• Model-driven approaches such as machine learning and statistics can often benefit
from uncovering patterns that cannot be seen by humans (due to the size and com-
plexity of the datasets)
• Model-driven approaches can avoid human and emotional biases (as long as the
correct processes are carefully applied)
However, there is no reason why both model-driven and human-driven processes and de-
cision making cannot coexist. For example, many machine learning systems rely on receiv-
ing labeled data in order to train models. Often, labeling such data is costly, time consum-
ing, and requires human input. A good example of this is classifying textual data into cat-
egories or assigning a sentiment indicator to the text. Many real-world systems use some
form of human-driven system to generate labels for such data (or at least part of it) to
provide training data to models. These models are then used to make predictions in the live
system at a larger scale.
In the context of MovieStream, we need not fear that our machine learning system will
make the content team redundant. Indeed, we will see that our aim is to lift the burden of
time-consuming tasks where machine learning might be able to perform better while
providing tools to allow the team to better understand the users and content. This might, for
example, help them in selecting which new content to acquire for the catalogue (which in-
volves a significant amount of cost and is therefore a critical aspect of the business).
