Why We Use Google Cloud
At SSR R&D, we build trustworthy AI systems for SMBs. Google Cloud gives us the performance, guardrails, and speed we need—so we can prototype quickly, ship safely, and keep costs predictable.
Data Ingestion & Modeling with BigQuery
BigQuery is our analytics backbone. Serverless and massively parallel, it lets us ingest from spreadsheets, SaaS exports, and operational databases, then model everything centrally. Storage and compute scale independently, which keeps performance high and spend under control.
Rapid Prototyping with BigQuery ML
With BigQuery ML, we can build baselines directly in SQL—classification, forecasting, and clustering—without moving data. That means faster iteration, cleaner governance, and less integration overhead.
Advanced AI with Vertex AI
When use cases get more custom, we move to Vertex AI for the full ML lifecycle: feature engineering, training (including TPUs), evaluations, model registry, and one-click deployments. Versioning and experiment tracking make our MLOps auditable.
Serverless Agility with Cloud Run
Cloud Run lets us deploy containerized backends and evaluators that scale to zero when idle and surge under load. It’s perfect for AI adapters, lightweight APIs, and scheduled jobs—fast to ship and easy to maintain.
Secure & Observable Pipelines
We apply least-privilege access with Cloud IAM, keep an eye on systems with Cloud Monitoring and Cloud Logging, and enforce budgets/alerts to avoid surprises. For sensitive data, we layer in redaction and policies to meet regulatory expectations.
Conclusion
Google Cloud’s tight integration across data and ML accelerates time-to-value without sacrificing governance. The result: SMBs get production-ready AI that’s fast, safe, and measurable.