Utilize Google Kubernetes Engine (GKE) to deploy a custom normalization service, allowing for flexible scaling and management of normalization tasks.

Implement the normalization algorithm directly in SQL within BigQuery, leveraging its powerful query engine for efficient data processing.

Use TensorFlow’s Feature Column normalizer_fn within your model to handle normalization during the training phase, avoiding preprocessing steps.

Deploy Apache Spark on Dataproc with the BigQuery connector to perform normalization, taking advantage of Spark's distributed computing capabilities.

Combine the use of BigQuery's SQL for initial data filtering and aggregation with TensorFlow’s normalizer_fn for feature-specific normalization within the model.