Google Cloud Platform

Google Cloud has specific advantages for ML-heavy workloads: Vertex AI provides a unified MLOps platform with AutoML, custom training, and managed endpoints; BigQuery ML enables in-database model training and inference; TPUs offer the best price-performance for large model training; and Vertex AI Workbench provides a managed notebook environment. We recommend GCP when you need Vertex AI Pipelines, BigQuery-native ML, or TPU access for training foundation models.

A production GCP ML architecture includes: Vertex AI for training, evaluation, and serving; Vertex AI Model Registry for versioning and governance; Pub/Sub and Dataflow for real-time feature pipelines; BigQuery for feature storage and batch inference; Cloud Monitoring for observability; and IAM with VPC Service Controls for security. We deploy with Terraform and use Vertex AI Pipelines for automated training and promotion workflows.

A complete Vertex AI MLOps setup — training pipeline, model registry, serving endpoint, and monitoring — typically takes 6-10 weeks. Migrating an existing ML system to GCP infrastructure takes 4-8 weeks. For teams starting from scratch on GCP with a new model and pipeline, end-to-end delivery runs 8-12 weeks including data pipeline work.