What is involved in the model consumption step of the Data Science workflow?

The model consumption step of the Data Science workflow focuses on making the trained model accessible for use within applications or by end users. This involves packaging and publishing the model as a service, which allows it to be integrated into other systems where it can be utilized to generate predictions or insights based on new data. By packaging the model, data scientists ensure that it can be easily invoked and that it performs consistently across different environments.

Deploying the model to a production environment, while an important step, generally falls under the broader scope of model implementation rather than consumption. Adjusting model parameters typically occurs during the training phase, not during consumption. Documenting model findings is essential for understanding model performance and decisions but is more aligned with the reporting and evaluation stage, not the practical use of the model.