This is a dedicated watch page for a single video.
You aim to redesign your ML pipeline for structured data on Google Cloud. Currently, you employ PySpark for large-scale data transformations, but your pipelines take over 12 hours to run. To accelerate development and pipeline execution, you intend to leverage a serverless tool and SQL syntax. With your raw data already migrated to Cloud Storage, how should you construct the pipeline on Google Cloud to meet the speed and processing requirements?