ml-pipeline-workflow
Design Production ML Pipelines
Production ML workflows often fail because data, training, validation, deployment, and monitoring are designed separately. This skill provides a structured lifecycle and orchestration guidance.
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Review the Skillstore skill "ml-pipeline-workflow" from https://skillstore.io/skills/sickn33-ml-pipeline-workflow.md and its manifest at https://skillstore.io/api/skills/sickn33-ml-pipeline-workflow/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
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Test it
Using "ml-pipeline-workflow". Design a batch churn-model pipeline with daily training and weekly deployment.
Expected outcome:
- Daily: ingest snapshots, validate schema and quality, build versioned features, train candidates, and record experiments.
- Weekly: compare the best candidate with production, require approval, deploy a canary, monitor metrics, and preserve rollback.
Using "ml-pipeline-workflow". How should a team detect model regressions before deployment?
Expected outcome:
Compare candidates against a fixed baseline on versioned data. Gate deployment on predictive quality, fairness, latency, stability, and business thresholds.
Using "ml-pipeline-workflow". Review a pipeline that retrains automatically after drift alerts.
Expected outcome:
Validate alert quality and incoming data before training. Require candidate comparison, controlled rollout, monitoring, and rollback before replacing production.
Security Audit
SafeAll ten static findings are false positives caused by Markdown inline code markers and fenced code blocks. The reviewed content contains guidance and declarative examples, not shell execution.
Risk Factors
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APA citation
sickn33. (2026). ml-pipeline-workflow security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-ml-pipeline-workflow/audits/5BibTeX citation
@techreport{sickn33-sickn33-ml-pipeline-workflow-2026,
author = {sickn33},
title = {ml-pipeline-workflow security audit report (audit version 5)},
institution = {Skillstore},
year = {2026},
number = {5},
url = {https://skillstore.io/skills/sickn33-ml-pipeline-workflow/audits/5},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "ml-pipeline-workflow security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-08-04"
url: "https://skillstore.io/skills/sickn33-ml-pipeline-workflow/audits/5"
identifiers:
- type: other
value: "skillstore:sickn33-ml-pipeline-workflow:audit:5"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
wshobson-ml-pipeline-workflow
2026-08-21
sickn33-ml-pipeline-workflow
2026-08-21
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Plan a New ML Platform
Map lifecycle stages, dependencies, validation gates, and deployment controls before implementation.
Operationalize a Research Model
Convert an experimental training process into a reproducible workflow with tracking, validation, and rollback.
Review Pipeline Reliability
Assess an existing workflow for observability, idempotency, lineage, retries, and production monitoring.
Try These Prompts
Design a simple ML pipeline for [use case]. Include ingestion, validation, training, evaluation, deployment, and monitoring stages.
Compare Airflow, Dagster, Kubeflow, and Prefect for [environment]. Recommend one based on scale, infrastructure, team skills, and operational needs.
Review this pipeline design: [design]. Add data checks, lineage, experiment tracking, approval gates, retries, observability, and rollback controls.
Create a continuous training architecture for [model]. Define triggers, data validation, comparison gates, canary rollout, drift monitoring, and automated rollback.
Best Practices
- Version data, code, configuration, and model artifacts together for reproducibility.
- Make every stage idempotent, independently testable, observable, and retry-aware.
- Use explicit validation and approval gates before production deployment.
Avoid
- Do not deploy a newly trained model without baseline comparison and rollback planning.
- Do not hide data transformations inside training jobs without lineage or versioning.
- Do not automate retraining from drift alerts without validating data and alert quality.
Frequently Asked Questions
Does this skill execute an ML pipeline?
Which orchestrators does it cover?
Does it support cloud deployments?
Can it help with continuous training?
Does it include ready-to-run templates?
What information should I provide?
Developer Details
Author
sickn33License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Repository
https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/ml-pipeline-workflowRef
81e05e636292629114b76cbb3922fbe57672fc02
Maintenance freshness
8/5/2026
Usage
10 downloads · 112 views
File structure
📄 SKILL.md