What happens when an ML pipeline's status is 'Running'?

What happens when an ML pipeline's status is 'Running'?

• A. The pipeline is waiting for a license
• B. The pipeline is executing and can be killed
• C. The pipeline has completed all tasks successfully
• D. The pipeline is queued and will start soon

Answer: (B)

When an ML pipeline's status is 'Running', it indicates that the pipeline is currently executing its tasks as part of the machine learning workflow. During this phase, various processes such as data ingestion, model training, and evaluation are actively taking place. It is also possible for a running pipeline to be terminated or "killed" if necessary—perhaps due to resource allocations or operational decisions—allowing for control over the execution flow. This status is distinct from other stages of the pipeline lifecycle. For instance, if a pipeline were waiting for a license, it would be in a blocked state rather than actively processing tasks. Similarly, a completed pipeline would have transitioned to a status that reflects successful completion, while a queued pipeline would not have started execution yet and would be awaiting resources or designated time to begin processing. Understanding this status helps in managing and monitoring machine learning operations effectively.

When an ML pipeline's status is 'Running', it indicates that the pipeline is currently executing its tasks as part of the machine learning workflow. During this phase, various processes such as data ingestion, model training, and evaluation are actively taking place. It is also possible for a running pipeline to be terminated or "killed" if necessary—perhaps due to resource allocations or operational decisions—allowing for control over the execution flow.

This status is distinct from other stages of the pipeline lifecycle. For instance, if a pipeline were waiting for a license, it would be in a blocked state rather than actively processing tasks. Similarly, a completed pipeline would have transitioned to a status that reflects successful completion, while a queued pipeline would not have started execution yet and would be awaiting resources or designated time to begin processing. Understanding this status helps in managing and monitoring machine learning operations effectively.