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Logs for an ML Pipeline serve as a comprehensive record of various events and actions that take place throughout the pipeline's lifecycle. They provide valuable insights into each status change, capturing the progression from the initiation of the pipeline through to its completion. These status updates are crucial for monitoring the performance and health of the pipeline, allowing practitioners to understand how the pipeline transitions between different phases, such as training, validating, and deploying machine learning models. By tracking these changes, users can identify where the process stands at any given moment, which is essential for maintaining operational oversight and for troubleshooting if issues arise.

While successful execution duration, errors during deployment, and validation information are also important aspects of an ML Pipeline, they do not encompass the broader scope of what the logs represent. Logs specifically highlight the evolving status at each point in the pipeline's operational journey, making them a vital tool for anyone involved in the development and deployment of machine learning solutions.