The question presents a scenario where an ML pipeline needs to be orchestrated across various Google Cloud services, triggered only by the addition of new files to a Cloud Storage bucket. The goal is to minimize costs.
The suggested answer is C. This option combines the scalability of Vertex AI Pipelines with the event-driven nature of Cloud Functions, efficiently triggering pipeline execution only when necessary, thus minimizing cost.
You are creating an ML pipeline for data processing, model training, and model deployment that uses different Google Cloud services. You have developed code for each individual task, and you expect a high frequency of new files. You now need to create an orchestration layer on top of these tasks. You only want this orchestration pipeline to run if new files are present in your dataset in a Cloud Storage bucket. You also want to minimize the compute node costs. What should you do?
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