From Pilot to Production: ML on Google Cloud for Teams

Machine Learning on Google Cloud gives you a full stack for ML; from raw data to governed, monitored models without stitching a dozen tools together.

Core building blocks

• Data estate: Cloud Storage for lakes, BigQuery for analytics warehousing, Dataproc for Spark, Dataflow for streaming/batch pipelines, Pub/Sub for events.

• Model platform: Vertex AI unifies training, tuning, inference, registries, Feature Store, Experiments, Pipelines, and Model Monitoring.

• Integrated ML: BigQuery ML trains models with SQL (forecasting, classification, regression) and serves them right where the data lives.

Patterns that work in practice

• Analytics → ML loop: Explore in BigQuery, prototype with BigQuery ML, then promote to Vertex AI for advanced training/serving.

• RAG & GenAI: Use Vertex AI’s embedding/text models, Vector Search, and private corpora in BigQuery/Cloud Storage to ground answers securely.

• Real-time inference: Stream features via Pub/Sub → Dataflow; serve models on Vertex AI Endpoints or lightweight Cloud Run services.

MLOps essentials

• CI/CD for ML: Use Vertex AI Pipelines (Kubeflow under the hood) to codify data prep, training, evals, and deployment with approvals.

• Governance: Central Model Registry, lineage (Data Catalog), eval reports, canary/blue-green rollouts, and continuous drift monitoring.

• Observability: Cloud Logging/Monitoring for latency, cost, and quality; define SLOs per model.

Security & compliance

• IAM-first design with Workload Identity; VPC-SC for data perimeters.

• Encrypt at rest/in transit; lock service accounts and secrets.

• Private service access for training on sensitive data.

Cost controls

• Start in BigQuery ML to avoid data egress.

• Use spot/TPU/GPU autoscaling for training; right-size online endpoints; cache features; purge old artifacts.

Starter reference architecture

1. Land data in GCS → curate in BigQuery (dbt or Dataform).

2. Prototype with BQML; log metrics to BigQuery.

3. Productionize on Vertex AI: Feature Store, Pipelines, Registry, Endpoints.

4. Policy guardrails, audits, dashboards, and alerts from day one.