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Announcing BigQuery and BigQuery ML operators for Vertex AI Pipelines

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Announcing BigQuery and BigQuery ML operators for Vertex AI Pipelines

Google Cloud announced new BigQuery and BigQuery ML (BQML) operators for Vertex artificial intelligence (AI) Pipelines on April 5, 2022, enabling machine learning (ML) engineers to orchestrate data and model operations without building custom components. The official components support query execution, model creation, evaluation, prediction, and export workflows, with automatic artifact tracking and governance through Vertex ML Metadata. An end-to-end example demonstrates training a document classifier using Dataflow for preprocessing, BQML for embedding generation, and logistic regression for topic prediction.

Why it matters: ML engineers using Google Cloud infrastructure can now operationalize BigQuery and BQML workloads more efficiently within Vertex AI Pipelines, reducing the time spent building custom integrations for model lifecycle automation and governance.

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threat intel

Announcing BigQuery and BigQuery ML operators for Vertex AI Pipelines

Google Cloud announced new BigQuery and BigQuery ML (BQML) operators for Vertex artificial intelligence (AI) Pipelines on April 5, 2022, enabling machine learning (ML) engineers to orchestrate data and model operations without building custom components. The official components support query execution, model creation, evaluation, prediction, and export workflows, with automatic artifact tracking and governance through Vertex ML Metadata. An end-to-end example demonstrates training a document classifier using Dataflow for preprocessing, BQML for embedding generation, and logistic regression for topic prediction.

Why it matters: ML engineers using Google Cloud infrastructure can now operationalize BigQuery and BQML workloads more efficiently within Vertex AI Pipelines, reducing the time spent building custom integrations for model lifecycle automation and governance.

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