Business use cases for a machine learning system

Perhaps the first question we should answer is, Why to use machine learning at all?

Why doesn't MovieStream simply continue with human-driven decisions? There are many reasons 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 infeasible 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 complexity 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 decision making cannot coexist. For example, many machine learning systems rely on receiving labeled data in order to train models. Often, labeling such data is costly, time consuming, and requires human input. A good example of this is classifying textual data into categories 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 catalog (which involves a significant amount of cost and is, therefore, a critical aspect of the business).