AI-generated code risks are often not obvious syntax errors. The harder problem is code that catches real failures, retries them badly, returns mock data and still reports success.
A practical guide to production ML delivery on Databricks: where GitHub Actions fits, why Declarative Automation Bundles should usually be the deployment contract, and what to standardize before the first real production rollout.
A reference architecture for building one AI platform across BigQuery, Databricks and Vertex AI without creating new silos.
About me
Architecture, delivery and AI systems that have to hold up in production
I am a Data & AI Architect working on production data platforms, MLOps and cloud-native AI systems. My work focuses on the engineering decisions that make these systems operable, observable and maintainable.
