When you need this
- Retrain depends on a laptop cron
- No visibility into failed batch jobs
- Need retries and concurrency limits for feature builds
- Want Python-native orchestration without Airflow ops weight
Prerequisites
- Prefect server/cloud and a work pool
- One critical pipeline (retrain or batch score)
- Secrets for data + MLflow access
Tools
Trial for mid-market ML teams; Dagster assess when asset lineage is the priority.
Trial once datasets leave laptops; pair with registry for model promotion.
Start with registry + experiment tracking before full deployment automation.
Trial before auto-retrain; bad data amplification is the #1 silent failure mode.
Steps
- 1
Model the flow
Tasks: validate data → train/score → log to MLflow → notify. Idempotent tasks with clear inputs/outputs.
- 2
Add data quality and versioning
Great Expectations before heavy compute; DVC hashes logged as flow parameters.
- 3
Schedule and alert
Deployments with cron/schedules. Alert on failure to the owning team channel. Set concurrency limits on GPU work pools.
- 4
Observe and improve
Dashboard success rate and duration. Promote the flow into the golden path once stable.
Adoption pitfalls
- Giant single task that cannot retry safely
- No data validation → silent bad retrain
- Schedules without ownership → zombie flows
Adoption checklist
- Critical pipeline runs on schedule without laptop dependency
- Failures alert the owning team
- MLflow run linked from every train flow
- GPU concurrency limits prevent cluster stampede
SEER REAL assessment / sprint
Assessment finds fragile retrain paths. Sprint moves one pipeline to Prefect with quality gates and on-call alerts.