What is the focus of model deployment?

The focus of model deployment is centered on making trained models available for use. After a model has been developed and trained using a training dataset, the next critical step is to ensure that this model can be utilized in real-world applications or production environments. This involves creating APIs, setting up endpoints, or integrating the model into software applications so that end-users or other systems can interact with it to make predictions or decisions based on new input data.

Effective model deployment requires considering factors such as scalability, performance, monitoring, and maintenance of the model once it's in production. The goal is to bridge the gap between the development phase and operational use, ensuring models deliver value in practical settings.

Options focusing solely on training models, automating data ingestion, or writing code in notebooks do not address the broader purpose of deploying models in a usable format. They represent earlier phases of the data science workflow rather than the end goal of making those models operational.