Unified Data & AI Platform
One platform for the full ML lifecycle — experimentation, training, deployment, real-time inference, CI/CD, observability and governance — shared across teams.
- ~60%faster deployment cycles
- 1platform shared across teams
Architecture · simplified
- DataSnowflake as the source of truth
- Experiment & trainReproducible, tracked runs
- OrchestratePrefect pipelines
- Deploy & serveCI/CD, real-time inference on AWS
- Observe & governMonitoring, lineage, controls
Context
As the number of models and teams grew, each project was rebuilding the same plumbing — its own pipelines, its own deployment scripts, its own idea of monitoring. Shipping was slow and running things in production was fragile.
The challenge
Build a shared foundation that data scientists actually want to use, without slowing them down — and that engineering, security and governance teams can trust.
What I did
- Designed and built a unified Data & AI platform on Snowflake, AWS and Prefect, covering experimentation, training, deployment, real-time inference, CI/CD, observability and governance.
- Established production engineering practices — CI/CD for models, observability, model lifecycle management and reusable infrastructure — as defaults rather than afterthoughts.
- Led the data science and ML engineering team that built and ran it, and set the standards teams adopted.
Outcome
Deployment cycles dropped by ~60%, reliability improved, and new use cases start from a paved road instead of a blank page.