Chapter 2. Developing and Deploying Models
To understand the key components of MLOps for business and subject matter experts, it’s essential to first have a baseline understanding of how machine learning works. At its core, ML is the science of computer algorithms that automatically learns and improves from experience rather than being explicitly programmed. The algorithms analyze sample data—known as training data—to build a software model that can make predictions. ML algorithms can tackle problems that were either infeasible or too costly with previous software algorithms.
For example, an image recognition model might be able to identify the type of electricity meter from a photograph by searching for key patterns in the image that distinguish each type of meter. Another concrete example is an insurance recommender model, which might suggest additional insurance products that a specific existing customer is most likely to buy based on the previous behavior of similar customers.
When faced with unseen data, be it a photo or a customer, the ML model uses what it has learned from previous data to make the best prediction it can based on the assumption that the unseen data is somehow related to the previous data.
With the basics out of the way, this chapter will move on to more detailed components of ML model building and identify points in this process where business insights can provide particular value to a technical team.