A large organization is migrating its ML and data workloads to Google Cloud. Data is in Avro format in Cloud Storage. The goal is to design a pipeline for analytics, feature creation, and online prediction.
The suggested answer is B. This approach leverages BigQuery's strengths for analytics and Dataflow's scalability for feature engineering, while utilizing Vertex AI Feature Store for efficient online prediction.
You work at a large organization that recently decided to move their ML and data workloads to Google Cloud. The data engineering team has exported the structured data to a Cloud Storage bucket in Avro format. You need to propose a workflow that performs analytics, creates features, and hosts the features that your ML models use for online prediction. How should you configure the pipeline?
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