What does the model prototyping process encompass?

The model prototyping process primarily revolves around creating and refining machine learning models before they are deployed for real-world use. It encompasses key activities such as algorithm selection, where the most suitable machine learning algorithms are chosen based on the problem and data at hand. Following this, model training takes place, where the algorithm learns from the provided data to make predictions or classifications.

Hyperparameter tuning is a crucial step within this process, as it optimizes the model's performance by adjusting parameters that govern the algorithm's learning process. Lastly, model evaluation is essential to assess how well the model performs using metrics that reflect its accuracy and effectiveness, thus ensuring that the model is robust before it moves into deployment.

In the context of model prototyping, this phase focuses specifically on the iteration and refinement of the model itself, aiming to produce the best version ready for the subsequent stages, such as deployment. Other choices, while relevant in the larger machine learning lifecycle, do not accurately encapsulate the specific processes involved in the prototyping phase.