A Kubeflow pipeline for an end-to-end PyTorch-based MLOps workflow takes over an hour to complete. The pipeline involves reading data from BigQuery, processing data, feature engineering, model training, model evaluation, and deploying the model to Cloud Storage. The goal is to reduce pipeline execution time and cost.
The suggested answer is B. Enabling caching in all steps of the Kubeflow pipeline is the most efficient solution for speeding up execution and reducing cost without compromising the integrity of the process.
You are using Kubeflow Pipelines to develop an end-to-end PyTorch-based MLOps pipeline. The pipeline reads data from BigQuery, processes the data, conducts feature engineering, model training, model evaluation, and deploys the model as a binary file to Cloud Storage. You are writing code for several different versions of the feature engineering and model training steps, and running each new version in Vertex AI Pipelines. Each pipeline run is taking over an hour to complete. You want to speed up the pipeline execution to reduce your development time, and you want to avoid additional costs. What should you do?
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