Which combination best represents the steps involved in a typical machine learning workflow?

The selected answer represents a comprehensive overview of the typical steps involved in a machine learning workflow, highlighting the crucial phases that contribute to building and deploying a machine learning model effectively.

Data Preparation is often the foundational step in a machine learning workflow, where raw data is cleaned, transformed, and preprocessed. This ensures that the quality of the data is sufficient for training, as this phase may involve handling missing values, normalizing data, or conducting exploratory data analysis to derive insights.

Following data preparation, Model Training occurs where the actual machine learning algorithm is applied to the prepared dataset. During this phase, the model learns from the data by identifying patterns and relationships that can help in making predictions. This step is critical as the quality of training directly impacts the model’s performance.

Finally, Model Serving is the deployment phase where the trained model is made available for use in real-time applications or batch processes. This involves integrating the model into the existing system architecture, enabling it to receive new data inputs, make predictions, and return results continuously.

This combination effectively captures the essence of a machine learning project, from preparing the data to deploying the model, which is why it is the ideal representation of the typical machine learning workflow.