Companies adopting AI in the cloud often face a tool-selection dilemma: BigQuery for analytics, Databricks for data processing and MLOps, Vertex AI for training and serving models. Each has its strengths, but without cohesive integration you end up with silos, rising costs and governance headaches.

In this presentation, I’ll walk through a project where we built a complete AI platform in Google Cloud for a major financial institution, bringing BigQuery, Databricks and Vertex AI together into a single ecosystem. I’ll show the reference architecture, integration with CI/CD and private Git repositories, Terraform-based IaC, and how we tackled challenges around security, networking and compliance.

Participants will learn the key architectural decisions, the biggest pitfalls (IAM, quotas, costs), and the lessons that will help them implement similar solutions faster, safer and cheaper.