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BigQuery object tables are now generally available

BigQuery object tables are now generally available


With object tables, you can leverage the simplicity of SQL to run a wide range of AI models on unstructured data. There are three main mechanisms for using AI models. All enabled through the BigQuery inference engine.

First, import the model and query the object table to process the data within BigQuery. This approach is suitable for customers looking for an integrated BigQuery solution that can leverage their existing BigQuery resources. Since preview, we have extended our support beyond TensorFlow models with TF-Lite and ONNX models and introduced new scalar functions for image preprocessing. We also added support for storing preprocessed tensors, enabling efficient multi-model use of tensors and reducing slot usage.

Then you can choose from a variety of pre-trained Google models such as Cloud Vision API, Cloud Natural Language API, and Cloud Translation API. These models have additional predefined SQL table-valued functions to call when querying the object table. Inference results are stored as BigQuery tables.

Third, you can use remote capabilities to integrate customer-hosted AI models or custom models built through Vertex AI. When you call these remote functions from BigQuery SQL to provide objects to your model, the results are returned as a BigQuery table. This option is suitable if you run your own model infrastructure, such as a GPU, or if you maintain your model externally.

During preview, customers used a combination of these integration mechanisms to integrate AI workloads with data already in BigQuery. For example, agricultural technology company Semios uses imported remote imaging models to address precision agriculture use cases.

The new Import Models feature with Object Table allows you to import state-of-the-art Pytorch vision models to process image data and use BigQuery to improve temperature predictions in orchards. The new remote model feature can greatly simplify your pipeline and improve maintainability. – Semios




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