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A data scientist has developed a BigQuery ML model and needs your help to set up an ML pipeline for serving predictions. Your REST API application must provide predictions for a single user ID with a latency of less than 100 milliseconds, using the query: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL ‘dataset.model‘, table user_features). How can you establish the ML pipeline to meet these requirements?
A data scientist has developed a BigQuery ML model and needs your help to set up an ML pipeline for serving predictions. Your REST API application must provide predictions for a single user ID with a latency of less than 100 milliseconds, using the query: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL ‘dataset.model‘, table user_features). How can you establish the ML pipeline to meet these requirements?
Real Exam
A
Create a Dataflow pipeline using BigQueryIO to read predictions for all users from the query. Write the results to Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Bigtable.
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