An architecture for a machine learning system

Now that we have explored how our machine learning system might work in the context of MovieStream, we can outline a possible architecture for our system:

As we can see, our system incorporates the machine learning pipeline outlined in the preceding diagram; this system also includes:

• Collecting data about users, their behavior, and our content titles
• Transforming this data into features
• Training our models, including our training-testing and model-selection phases
• Deploying the trained models to both our live model-serving system as well as using these models for offline processes
• Feeding back the model results into the MovieStream website through recommendation and targeting pages
• Feeding back the model results into MovieStream's personalized marketing channels
• Using the offline models to provide tools to MovieStream's various teams to better understand user behavior, characteristics of the content catalogue, and drivers of revenue for the business

In the next section, we digress a little from Movie Stream and give an overview of MLlib-Spark's machine learning module.