Google Cloud · Retail
Retail analytics platform doubles model velocity with BigQuery + Vertex AI
2x model deployment velocity · MLOps on BigQuery + Vertex AI
At a glance
Key metrics
2x
model deployment velocity
30%
inference-cost reduction
400+
brands on shared platform
Challenge
The situation
Model deployment required hand-tuning per customer. Inference costs ballooned with customer growth. ML engineers spent most of their time firefighting.
Approach
How we delivered
- 01 Rebuilt the ML platform on BigQuery + Vertex AI with repeatable training pipelines.
- 02 Adopted Vertex AI Model Registry + Endpoints with auto-scaling.
- 03 Standardized feature engineering via BigQuery ML.
Architecture
Solution architecture
BigQuery as feature store and training data warehouse. Vertex AI Pipelines for repeatable training. Vertex AI Endpoints for serving. Looker for customer-facing analytics.
Architecture diagram placeholder
Data flow across source systems, platform, governance, and activation layers.
Outcomes
Measured results
- 2x faster model deployment.
- 30% inference-cost reduction.
- New-customer onboarding reduced from weeks to days.
Technology
Tech stack
Platform
- Google BigQuery
- Vertex AI Pipelines
- Vertex AI Endpoints
- BigQuery ML
Analytics
- Looker
“Our engineers finally build platforms, not one-off deployments.”
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