When you need this
- Five teams, five different ML deploy patterns
- New hires take months to ship first model
- Platform team drowning in one-off requests
- Security findings differ wildly by team
Prerequisites
- Backstage instance or IDP
- One reference architecture agreed (gateway, OTel, registry)
- Platform owner with capacity to maintain the template
Tools
Trial with one golden path template - not a full portal rewrite.
Deploy as single ingress before adding a second LLM vendor.
Instrument gateway and app tier first; expand to training jobs later.
Start with registry + experiment tracking before full deployment automation.
Steps
- 1
Pick the thinnest viable path
One template: FastAPI + LiteLLM + OTel + MLflow registry + cost tags. Document tradeoffs, not every option.
- 2
Create Backstage software template
Scaffold repo, CI, K8s manifests, observability defaults, and PII gate hooks in one click.
- 3
Measure adoption
Track % of new AI services created from the template vs bespoke. Target ≥80% within two quarters.
- 4
Iterate from audit findings
Add controls (eval CI, Presidio, FOCUS tags) to the template as recurring gaps appear in assessments.
Adoption pitfalls
- Template includes 12 optional stacks — nobody uses it
- No owner → template rots in 6 months
- Golden path without a migration path for existing services
Adoption checklist
- Template documented with owner
- New service default is golden path
- Template includes observability and cost tags
- Quarterly template review scheduled
SEER REAL assessment / sprint
Assessment finds pattern sprawl. Sprint delivers one golden-path template and migrates a pilot team onto it.