Which stage of the ML workflow focuses on optimizing the model's performance by making adjustments to the model based on its accuracy?

The stage of the machine learning workflow that focuses on optimizing the model's performance by making adjustments to the model based on its accuracy is the model training phase. During model training, the algorithm learns from the training data, and this is where hyperparameters can be fine-tuned and the model structure can be adjusted to improve accuracy. The training process typically involves iterating over the data and optimizing the loss function, which guides how well the model is performing.

Model training is crucial because it is the phase where the model is directly exposed to the data and uses that interaction to learn generalizable patterns. By validating the model's performance against a validation dataset, you can assess its accuracy and make informed decisions about adjustments needed to enhance its predictive capability. This iterative process of training, validating, and tuning is essential for achieving optimal performance in machine learning models.

On the contrary, model serving pertains to deploying the trained model into a production environment where it can make predictions based on new data. Feature engineering involves selecting and transforming input data attributes to improve model performance but does not focus on the optimization of the model itself. Data preparation is about cleaning and organizing data before feeding it into the model training phase, making it a preliminary step that does not directly influence the model's