Which is not one of the steps in the machine learning workflow?

In the context of a machine learning workflow, the correct answer highlights that "Publish as a Product" is not typically considered a core step in the workflow itself. The machine learning workflow generally includes essential phases such as data collection and preparation, building a model, and model consumption, each of which contributes to the iterative process of developing, validating, and implementing machine learning models.

The steps of building a model and model consumption are fundamental aspects of the workflow. Building a model involves creating algorithms that learn from data, while model consumption refers to applying the trained model to new data to make predictions or inform decision-making.

Creating a recipe is a relevant step as it often refers to formulating the specific procedures and configurations necessary to train the model, ensuring that the data is appropriately prepared for analysis.

In contrast, "Publishing as a Product" generally pertains to the deployment and distribution of the machine learning model or application, which, while important in a broader project context, isn't a step in the workflow itself. Thus, this distinction clarifies why it is not part of the core machine learning workflow steps.