Product Manager for Data/AI Platforms
You own: what GeneFlow ships next, how it's priced, who it's for, and the metrics that prove it's working.
What's already shipped
See CHANGELOG.md. Six rounds:
- Schema + tracking + registry + prompts + REST + SDK + service extraction
- Artifacts + agent tools + dashboard UI + MLflow Projects
- K8s runner + compare UI + collab editor backend + tests + marketing
- K8s Job controller + WS gateway + prompt editor frontend + CLI + import script
- Runner RBAC + MLflow importer (TS-native) + WS gateway dashboard + soak test
- Model serving + drift monitoring (closes the lifecycle)
Marketing positioning: docs/marketing/GENEFLOW-vs-MLFLOW.md.
Roadmap candidates (next 3–6 months)
| Priority | Item | Why |
|---|---|---|
| P0 | Canary / shadow traffic routing for endpoints | traffic_split_pct + shadow_version columns exist; need router |
| P0 | Auto-retrain on critical drift | We have alerts but not yet the loop |
| P1 | Feature Store engine | Lineage refs exist; build the actual online/offline store |
| P1 | SageMaker importer | After MLflow, second-largest migration source |
| P1 | Drift PSI per-feature thresholds | Currently per-endpoint only |
| P2 | Model card auto-generation from gf_model_versions | Compliance ask, EU AI Act |
| P2 | Webhooks on stage transitions | CI/CD integration |
| P2 | Notebook collab (WS gateway already supports kind=notebook) | Reuse the editor |
| P3 | Federated learning service (already extracted) | Build out |
| P3 | Custom drift metrics (JS divergence, KS for categoricals) | DQE feedback |
North-star metrics
- Adoption: tenants with > 100 runs/week
- Retention: tenants with > 10 endpoints in Production
- Quality: drift alerts acknowledged within 4 hours
- Migration win: tenants who migrated FROM MLflow, signed annual
Pricing levers
- Per-endpoint hourly (already tracked in
gf_endpoints.hourly_cost_usd) - Per-inference markup
- Storage tier (artifacts in S3, charged-back)
- Premium: SOC2 audit pack, HIPAA-compliant config, multi-region
Competitive frame
| Competitor | Beats us on | We beat them on |
|---|---|---|
| SageMaker | AWS-native depth, batch transforms | Multi-cloud, prompts, no Lambda-style cold starts |
| Vertex AI | Google ML stack tie-in | Multi-cloud, open-source-aligned |
| WhyLabs / Arize | Drift detection sophistication | Lifecycle integration in one product |
| LangSmith / Helicone | LLM-only depth | Classical ML + LLM in one product |