Skills machine-learning-ops-ml-pipeline
📦

machine-learning-ops-ml-pipeline

Content revision r1 Safe ⚙️ External commands

Build Production ML Pipelines

Production ML systems require coordinated data, training, deployment, and monitoring plans. This skill structures that work into practical MLOps phases.

Supports: Claude Codex Code(CC)
🥉 78 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "machine-learning-ops-ml-pipeline" from https://skillstore.io/skills/sickn33-machine-learning-ops-ml-pipeline.md and its manifest at https://skillstore.io/api/skills/sickn33-machine-learning-ops-ml-pipeline/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.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/sickn33-machine-learning-ops-ml-pipeline.md Signed manifest GET /api/skills/sickn33-machine-learning-ops-ml-pipeline/manifest Signed lockfile GET /api/skills/sickn33-machine-learning-ops-ml-pipeline/lockfile

Test it

Using "machine-learning-ops-ml-pipeline". Design an MLOps pipeline for fraud detection with batch training and real-time inference.

Expected outcome:

  • A phased architecture covering data ingestion, quality checks, feature store design, model training, deployment, and monitoring.
  • Recommended tools for experiment tracking, model registry, serving, orchestration, and drift detection.
  • Operational criteria for latency, uptime, rollback, retraining, and alert response.

Using "machine-learning-ops-ml-pipeline". Improve an existing ML service that has no model monitoring or rollback process.

Expected outcome:

  • A monitoring plan for data drift, model quality, latency, throughput, and business impact.
  • A deployment strategy with canary releases, automated rollback, and validation gates.
  • A runbook outline for incident response, retraining triggers, and ownership handoffs.

Security Audit

Safe
v4 • 7/7/2026 Open versioned report

The two static findings are false positives caused by Markdown backticks and a deployment option list. I found no prompt injection, exfiltration intent, or unsafe command execution in the reviewed SKILL.md content.

1
Files scanned
315
Lines analyzed
0
Review items
0
False positives ignored

Risk Factors

⚙️ External commands (1)
SKILL.md:25
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History →
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Open versioned report
Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

sickn33. (2026). machine-learning-ops-ml-pipeline security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-machine-learning-ops-ml-pipeline/audits/4

BibTeX citation

@techreport{sickn33-sickn33-machine-learning-ops-ml-pipeline-2026, author = {sickn33}, title = {machine-learning-ops-ml-pipeline security audit report (audit version 4)}, institution = {Skillstore}, year = {2026}, number = {4}, url = {https://skillstore.io/skills/sickn33-machine-learning-ops-ml-pipeline/audits/4}, 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: "machine-learning-ops-ml-pipeline security audit report (audit version 4)" version: "unspecified" type: report authors: - name: "sickn33" date-released: "2026-07-07" url: "https://skillstore.io/skills/sickn33-machine-learning-ops-ml-pipeline/audits/4" identifiers: - type: other value: "skillstore:sickn33-machine-learning-ops-ml-pipeline:audit:4" description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: High
55
Architecture
85
Maintainability
87
Content
69
Community
83
Spec Compliance

What You Can Build

Plan a New MLOps Platform

Create an end-to-end blueprint for data ingestion, training, serving, monitoring, and retraining.

Standardize Model Delivery

Turn ad hoc notebooks into repeatable pipelines with experiment tracking, model registry, and deployment gates.

Improve Production Reliability

Add monitoring, drift detection, rollback, and cost controls to existing model serving workflows.

Try These Prompts

Basic Pipeline Plan 
Design a production ML pipeline for a churn prediction model. Include data ingestion, training, deployment, monitoring, and success criteria.
Tooling Selection 
Design an MLOps pipeline for real-time recommendations using AWS, MLflow, Feast, Kubernetes, and Prometheus. Explain each major tooling choice.
Migration Roadmap 
Create a migration plan from notebook-based model development to automated pipelines with versioned data, model registry, CI/CD, and monitoring.
Advanced Operating Model 
Design a multi-cloud MLOps operating model with feature stores, canary releases, drift alerts, retraining triggers, incident runbooks, and cost controls.

Best Practices

  • Provide concrete data sources, target metrics, latency needs, and deployment constraints before using the skill.
  • Review generated architecture with data engineering, platform, security, and model owners.
  • Validate all proposed infrastructure, monitoring thresholds, and rollback steps in a staging environment.

Avoid

  • Do not use generic defaults when data volume, privacy rules, or latency targets are unknown.
  • Do not deploy generated pipeline code without tests, secrets review, and infrastructure review.
  • Do not treat model accuracy as the only success metric for production readiness.

Frequently Asked Questions

Does this skill write a complete ML pipeline automatically?
It provides structured plans, implementation guidance, and component recommendations. Teams still need to implement and verify the final system.
Which tools does it cover?
It references common MLOps tools such as MLflow, Weights and Biases, Feast, KServe, Seldon, Kubeflow, Airflow, and Prometheus.
Can it support cloud-specific designs?
Yes. The prompt can include AWS, Azure, GCP, or multi-cloud constraints for platform-specific recommendations.
Is this useful for early prototypes?
Yes. It can create a lightweight path from prototype to production while identifying which controls can wait.
Does it handle monitoring and drift detection?
Yes. The workflow includes model performance, data drift, concept drift, alerts, dashboards, and retraining triggers.
Can Claude, Codex, and Claude Code use this skill?
Yes. The report marks support for Claude, Codex, and Claude Code.

Developer Details

Author

sickn33

License

MIT

Skillstore revision

r1

Version notice

The author did not declare a version.

Repository

https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/machine-learning-ops-ml-pipeline

Ref

816c62b2546ddb1c6a0453e7c781b5e095117819

Maintenance freshness

7/18/2026

Usage

11 downloads · 105 views

File structure

📄 SKILL.md

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