An organization is training a machine learning model to make (Digital Leader)

Correct answer: C) An increase in training data.
A) An increase in storage capacity may be necessary for storing larger datasets, but it doesn't directly impact prediction accuracy. Storage capacity is more related to the ability to store and manage data rather than improving the performance of machine learning models.

B) Higher network bandwidth is important for efficiently transferring data between storage, processing, and training components of machine learning workflows. However, it's not a primary factor in improving prediction accuracy. Bandwidth affects the speed of data transfer but doesn't inherently enhance the quality of the data or the model itself.

C) Increasing the amount of training data is one of the most effective ways to improve the performance and accuracy of machine learning models. More diverse and representative data can help the model learn complex patterns and relationships, leading to better generalization and prediction capabilities. With larger and more varied datasets, the model can capture a wider range of scenarios and make more accurate predictions.

D) Faster CPU processors can accelerate model training and inference processes, but they may not necessarily improve prediction accuracy on their own. While faster processors can reduce training times and enable more iterations of model tuning, the fundamental factor influencing prediction accuracy is the quality and quantity of the training data.

Therefore, C) An increase in training data is the most likely option to improve the prediction accuracy of the organization's machine learning model.