How It Works
We assess your BigQuery data quality and schema, select the right BQML model type for your use case — regression, classification, clustering, time series, or ARIMA — and design the feature engineering plan.
We train the BigQuery ML model using your warehouse data, integrate it with Vertex AI agents, and set up Gemini in BigQuery for natural language SQL generation — turning your analysts into power users.
We deploy to production, configure model monitoring for drift and data quality, and set up automated retraining triggers — ensuring your models stay accurate as your data evolves.
What's Included
Train production ML models — regression, classification, clustering, time series forecasting, and anomaly detection — directly inside BigQuery using SQL, without moving data to a separate ML platform.
Deploy Gemini in BigQuery to enable natural language SQL generation — letting analysts query complex data with plain English questions instead of hand-crafted SQL, dramatically accelerating insight velocity.
Bridge BigQuery ML predictions into Vertex AI agents — enabling agents to trigger model inference, retrieve predictions, and incorporate ML outputs into multi-step reasoning workflows.
Integrate BigQuery ML predictions and Vertex AI agent outputs into Looker dashboards — adding AI-powered anomaly callouts, predictive KPIs, and natural language data exploration to existing BI tooling.
Build Dataflow pipelines to continuously feed feature data into BigQuery ML models — automating the ETL layer so models train on fresh, high-quality feature sets without manual intervention.
Configure Vertex AI Model Monitoring to detect feature drift and prediction drift in production BQML models — triggering alerts and automated retraining before model accuracy degrades silently.
Who It's For
Analytics and data engineering teams with mature BigQuery data assets who want to add ML predictions and AI agents without standing up a separate ML infrastructure platform.
Teams moving from static Looker or Data Studio dashboards to AI agents that surface insights proactively, answer natural language questions, and trigger actions based on data signals.
Enterprises with data residency or governance requirements — you need ML models trained entirely inside GCP with no data egress to external ML platforms or third-party APIs.