Build Production ML Pipelines
ML teams need repeatable pipelines that connect data preparation, training, validation, deployment, and monitoring. This skill guides Claude, Codex, and Claude Code through practical MLOps workflow design.
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Review the Skillstore skill "ml-pipeline-workflow" from https://skillstore.io/skills/wshobson-ml-pipeline-workflow.md and its manifest at https://skillstore.io/api/skills/wshobson-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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Using "ml-pipeline-workflow". Plan a pipeline for a churn prediction model.
Expected outcome:
A five-stage workflow covering data checks, feature generation, training, validation gates, and canary deployment with rollback criteria.
Using "ml-pipeline-workflow". Prepare a model release review.
Expected outcome:
Using "ml-pipeline-workflow". Select an orchestration platform for a small team.
Expected outcome:
A comparison of Airflow, Dagster, Kubeflow, and Prefect with recommended fit, risks, and implementation steps.
All nine static findings are false positives caused by Markdown inline code or fenced examples in SKILL.md. I found no prompt injection, data exfiltration intent, or unsafe external command behavior in the reviewed skill text.
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https://skillstore.io/skills/wshobson-ml-pipeline-workflow/audits/8?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report[](https://skillstore.io/skills/wshobson-ml-pipeline-workflow?utm_source=security_passport_badge)<a href="https://skillstore.io/skills/wshobson-ml-pipeline-workflow?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/wshobson-ml-pipeline-workflow/security.svg" alt="Skillstore security assessment" loading="lazy"></a><iframe src="https://skillstore.io/embed/skills/wshobson-ml-pipeline-workflow.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>wshobson. (2026). ml-pipeline-workflow security audit report (audit version 8) [Author version unspecified]. Skillstore. https://skillstore.io/skills/wshobson-ml-pipeline-workflow/audits/8@techreport{wshobson-wshobson-ml-pipeline-workflow-2026,
author = {wshobson},
title = {ml-pipeline-workflow security audit report (audit version 8)},
institution = {Skillstore},
year = {2026},
number = {8},
url = {https://skillstore.io/skills/wshobson-ml-pipeline-workflow/audits/8},
note = {Author version unspecified}
}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 8)"
version: "unspecified"
type: report
authors:
- name: "wshobson"
date-released: "2026-07-07"
url: "https://skillstore.io/skills/wshobson-ml-pipeline-workflow/audits/8"
identifiers:
- type: other
value: "skillstore:wshobson-ml-pipeline-workflow:audit:8"
description: "Skillstore immutable audit report identifier"
Each 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
Convert an experimental notebook into a staged training and validation workflow.
Plan orchestration, deployment, rollback, and monitoring patterns for production models.
Evaluate pipeline tools and rollout practices before selecting an implementation approach.
Create a simple machine learning pipeline for [use case]. Include data ingestion, training, validation, deployment, and monitoring stages.
Compare Airflow, Dagster, Kubeflow, and Prefect for my ML workflow. My constraints are [team size, cloud, latency, governance].
Design validation gates for a model release. Include data quality checks, baseline comparison, approval criteria, rollback triggers, and reports.
Build a production MLOps workflow for [model type] serving [traffic pattern]. Include DAG structure, experiment tracking, CI/CD, canary rollout, monitoring, and incident response.
Author
wshobsonLicense
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Ref
36e07d5e13068e5be64447e8f20b427cf2cbd21a
Maintenance freshness
7/18/2026
Usage
9 downloads · 184 views
File structure
📄 SKILL.md
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