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Skills mlops-engineer
📦
mlops-engineer
Content revision r2 Safe ⚙️ External commands
Design Reliable Production MLOps Systems
Production machine learning requires coordinated pipelines, infrastructure, governance, deployment, and monitoring. This skill provides structured MLOps architecture guidance across major clouds and toolchains.
Supports: Claude Codex Code(CC)
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
Review the Skillstore skill "mlops-engineer" from https://skillstore.io/skills/sickn33-mlops-engineer.md and its manifest at https://skillstore.io/api/skills/sickn33-mlops-engineer/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.
Test it
Using "mlops-engineer". Design an AWS pipeline for weekly retraining and controlled endpoint releases.
Expected outcome:
- Use SageMaker Pipelines for processing, training, evaluation, and model registration.
- Require validation metrics and approval before a canary endpoint receives traffic.
- Monitor latency, errors, drift, and business metrics with rollback thresholds.
- Store artifacts in versioned S3 locations and document recovery procedures.
Using "mlops-engineer". Plan monitoring for a real-time fraud model.
Expected outcome:
Monitor service health, feature freshness, prediction distributions, delayed labels, precision, recall, and financial impact. Define alert owners and rollback thresholds.
Using "mlops-engineer". Recommend controls for a regulated model registry.
Expected outcome:
Use role-based access, immutable lineage, signed artifacts, documented approvals, environment promotion gates, retention policies, and periodic access reviews.
Security Audit
Safev5 • 8/4/2026 Open versioned report
Both static findings are false positives caused by ordinary Markdown prose: a relative document reference and an Azure Event Grid mention. No executable command, reconnaissance instruction, prompt injection, or other intent-level security concern was found.
1
Files scanned
223
Lines analyzed
0
Review items
0
False positives ignored
Risk Factors
⚙️ External commands (1)
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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APA citation
sickn33. (2026). mlops-engineer security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-mlops-engineer/audits/5BibTeX citation
@techreport{sickn33-sickn33-mlops-engineer-2026,
author = {sickn33},
title = {mlops-engineer security audit report (audit version 5)},
institution = {Skillstore},
year = {2026},
number = {5},
url = {https://skillstore.io/skills/sickn33-mlops-engineer/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: "mlops-engineer security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-08-04"
url: "https://skillstore.io/skills/sickn33-mlops-engineer/audits/5"
identifiers:
- type: other
value: "skillstore:sickn33-mlops-engineer:audit:5"
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
Launch a Team MLOps Platform
Create a practical platform architecture for repeatable training, registry management, deployment, monitoring, and team ownership.
Productionize a Research Model
Turn an experimental model workflow into a tested, versioned, observable, and recoverable production process.
Govern Regulated ML Workloads
Plan controls, audit trails, approval gates, access management, and monitoring for regulated machine learning systems.
Try These Prompts
Outline a Starter MLOps Platform
Design a starter MLOps platform for [team and use case] on [cloud]. Include core components, workflow stages, ownership, and initial milestones.
Select Tracking and Registry Tools
Compare [candidate tools] for experiment tracking and model registration. Evaluate integration, governance, scaling, operating effort, cost, and migration risks.
Plan a Safe Model Release
Create a release plan for [model and service]. Include validation gates, canary deployment, monitoring thresholds, rollback criteria, approvals, and an operational runbook.
Architect Regulated Multi-Cloud MLOps
Design a multi-cloud MLOps architecture for [workload] under [regulations]. Address identity, data boundaries, lineage, resilience, observability, cost, and disaster recovery.
Best Practices
- Provide workload scale, latency, data sensitivity, compliance, budget, and recovery objectives.
- Define measurable validation, release, rollback, and monitoring criteria before choosing tools.
- Verify recommendations against current provider documentation and test them in a non-production environment.
Avoid
- Do not select a complex platform before defining operating requirements and team ownership.
- Do not promote models using only training metrics without data, service, and business validation.
- Do not place credentials in pipeline definitions, images, logs, prompts, or model artifacts.
Frequently Asked Questions
Does this skill deploy infrastructure?
No. It produces architecture guidance, implementation plans, checklists, and operational recommendations.
Which cloud platforms are covered?
It covers AWS, Azure, and GCP services, plus Kubernetes-based and multi-cloud patterns.
Which MLOps tools can it evaluate?
It covers orchestration, experiment tracking, registries, versioning, feature stores, CI/CD, monitoring, and infrastructure tools.
Can it help with regulated workloads?
Yes. It can plan governance, access controls, audit trails, data protection, approvals, and monitoring for stated compliance requirements.
What information improves the result?
Provide the cloud, workload, scale, latency, team skills, existing stack, budget, security needs, compliance scope, and recovery targets.
Does it replace environment testing or expert review?
No. Validate every design against current documentation, organizational policy, workload tests, security review, and production readiness criteria.
Developer Details
Author
sickn33License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
81e05e636292629114b76cbb3922fbe57672fc02
Maintenance freshness
8/5/2026
Usage
11 downloads · 116 views
File structure
📄 SKILL.md
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