The Art of Creating BigQuery ML Feature Magic Mastery

In this regard, BigQuery ML has advanced significantly, providing data scientists and ML developers with a flexible range of preprocessing tools for feature engineering

These conversions can also be smoothly included into models, guaranteeing their transferability from BigQuery to serving environments such as Vertex AI

A TRANSFORM statement may be included in the CREATE MODEL statement when building a model in BigQuery ML

This offers uniformity of preprocessing comparable to other frameworks notably the Transform component of the TFX framework, which helps avoid training/serving skew

The model saves the computed scaling parameters to apply later when using the model for inference, which is a benefit of the embedded scaling functions

This operates like other models by using CREATE MODEL with a TRANSFORM statement and using the variable model_type = TRANSFORM_ONLY

The TRANSFORM statement is assembled into a model using a standard CREATE MODEL statement

The feature pipeline is quite readable because to the BigQuery SQL Query syntax’s WITH clause (CTEs).

Building and maintaining machine learning pipelines and power MLOps may be made simpler with the help of BigQuery ML’s new reusable and modular feature engineering For more details