mlflow

The platform's model registry + experiment tracker. Owned by alphaswarm_models — every Predictor, AlphaBacktestExperiment, walk-forward run, and finetune trainer registers here.

Identity

Field	Value
Service id	mlflow
Role	mlops
Image	ghcr.io/mlflow/mlflow:v2.11.1
Port	5000
Storage	object store for artifacts (MinIO / S3 / GCS / ADLS depending on cloud); Postgres backend for the tracking store

Deployment surfaces

Surface	Where
Compose	service mlflow in alphaswarm_platform/compose/docker-compose.yml
Kustomize	deployments/kubernetes/base-services/mlflow/ — Deployment + Service + ExternalSecret-backed credentials
MLOps overlay	reachable through mlops/ when paired with Argo Workflows + Dagster

Dependencies

Upstream:

• postgres — tracking store.
• minio / s3 / gcs / azblob — artifact store.

Downstream:

• alphaswarm-core, alphaswarm-worker — every Predictor / Skill / walk-forward / finetune flow registers runs here.
• alphaswarm-ml-mcp — read paths surface through the data.ml.* MCP slice.

Operations

• Auth: behind the cluster ingress; the in-cluster URL is the only path. Local dev exposes http://localhost:5000 for browser inspection.
• Pruning: retention policy lives at alphaswarm/tasks/cleanup/mlflow_prune.py — run by beat weekly.
• Run tagging: every run is tagged with the originating experiment_id + test_id per AGENTS rule 34 so audit queries can correlate ML runs with strategy / backtest activity.

See also

• mlops-service.md — how alphaswarm_models lays MLflow underneath the Skill / Predictor contract.
• ml-framework.md — model framework overview.
• alphaswarm_models/AGENTS.md — boundary rules.