Self-paced · Start any time
AI training for platform engineers and DevOps
The infrastructure production AI runs on: pipelines, vector stores, observability, and deployment patterns that hold under load.
Once an AI feature works, it becomes an operations problem. This track covers the platform side: data pipelines, embedding refresh, caching, observability, and the failure modes that only appear at scale.
Where most platform engineers and DevOps are today
- Prototype pipelines promoted straight into production
- No visibility into embedding staleness or retrieval drift
- Model spend that spikes without an alert
What you can do after the program
- Repeatable ingest and embedding refresh pipelines
- Observability on quality, latency, and spend
- Deployment and rollback patterns for prompt and model changes
Skills covered
Data pipelinesVector storesObservabilityCaching strategiesCost controlsDeployment patterns
Recommended programs
Questions
What background is expected?
Working experience with cloud infrastructure or backend systems. No ML background required.
Is there a refund policy?
Yes — a 30-day refund window from enrolment.