What is the primary function of Amazon CloudWatch in ML workflows?

Amazon CloudWatch serves as a monitoring service that provides key insights into resource utilization and application performance in various AWS environments, including those utilizing machine learning. By tracking metrics such as CPU usage, memory consumption, and disk I/O, CloudWatch enables users to maintain optimal performance levels by setting up alarms and notifications based on specified thresholds. This is crucial in machine learning workflows, as monitoring these resources can help ensure that the underlying infrastructure remains stable and efficient during the model training and inference stages.

In contrast, other functions mentioned may be important in their respective contexts but do not align directly with the core purpose of Amazon CloudWatch. For instance, while visualizing outcomes and automating training are essential components of machine learning, these tasks are typically managed by different services specifically designed for data analysis or operation orchestration. Thus, the role of CloudWatch is primarily to provide real-time monitoring and metrics, ensuring that machine learning workflows operate smoothly without interruptions caused by resource constraints.