You developed a Vertex AI pipeline that trains a classification model on data stored in a large BigQuery table. The pipeline has four steps, where each step is created by a Python function that uses t

Changing YAML filenames is an organizational detail and does not inherently affect caching or reduce the computational costs of pipeline steps.

While `kubeflow.v1.caching: True` enables component caching, its effectiveness can be limited if the overall pipeline definition or pipeline run name changes frequently (e.g., due to `f"my-awesome-pipeline-{dt}"`), potentially causing cache misses.

Moving the data export and preprocessing steps to a separate, less frequent job that writes its output to Cloud Storage (a 'cached path') ensures that these expensive operations are not repeated during iterative development of the training step. The main pipeline then consumes this pre-processed data, significantly reducing costs during rapid iteration.

Changing the pipeline name with a dynamic timestamp (`f"my-awesome-pipeline-{dt}"`) would actually hinder KubeFlow Pipelines' default caching mechanism across runs, as each run would be considered a new, distinct pipeline, thereby increasing costs.