ml-mlops
ML MLOps guides you through building auditable, repeatable machine learning workflows. It covers experiment tracking, model versioning and governance, pipeline orchestration across Kubernetes, AWS, GCP, and Azure, CI/CD automation, and production monitoring to catch drift and quality issues.
ML MLOps helps you build production-grade machine learning systems with tracking, versioning, orchestration, and governance.
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Decision gist · record as of 2026-06-18
ML MLOps helps you build production-grade machine learning systems with tracking, versioning, orchestration, and governance. ML MLOps guides you through building auditable, repeatable machine learning workflows. It covers experiment tracking, model versioning and governance, pipeline orchestration across Kubernetes, AWS, GCP, and Azure, CI/CD automation, and production monitoring to catch drift and quality issues.
Use it when
- ML MLOps covers end-to-end production ML infrastructure including pipeline orchestration, CI/CD automation, safe deployment strategies.
- ML MLOps equips you with strategies to monitor model performance in production and detect data or feature drift that degrades predictions.
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JosiahSiegel/claude-plugin-marketplace/ml-mlops · repository language: Shell
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Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I track ML experiments and models with ml-mlops?
ML MLOps provides comprehensive experiment tracking and model versioning capabilities to maintain reproducibility and lineage. You can log parameters, metrics, artifacts, and model metadata throughout your workflow. The skill guides you through setting up centralized model registries, versioning strategies, and tracking systems that enable teams to compare experiments, reproduce results, and maintain a complete audit trail of model evolution from development through production.
What MLOps best practices does ml-mlops cover for production?
ML MLOps covers end-to-end production ML infrastructure including pipeline orchestration, CI/CD automation, safe deployment strategies, and governance controls. It teaches you how to implement repeatable workflows across Kubernetes, AWS, GCP, and Azure; automate model testing and validation; establish audit logs and compliance frameworks; and integrate monitoring to detect drift and quality issues. These practices ensure your ML systems remain auditable, reliable, and maintainable at scale.
How can ml-mlops help with drift detection and model monitoring?
ML MLOps equips you with strategies to monitor model performance in production and detect data or feature drift that degrades predictions. The skill covers setting up alerting systems, establishing performance baselines, and triggering automated retraining workflows when drift is detected. You'll learn how to implement continuous monitoring and quality checks that catch issues early, enabling proactive model updates before performance degradation impacts users.
What deployment strategies does ml-mlops recommend for ML models?
ML MLOps guides you through implementing safe, controlled deployment strategies including canary deployments, A/B testing, and gradual rollouts. The skill covers CI/CD pipeline automation for ML, model rollback procedures, versioning controls, and safe promotion from staging to production. You'll learn how to validate models before deployment, monitor performance during rollouts, and maintain the ability to quickly revert if issues arise.
How does ml-mlops address governance and compliance for AI systems?
ML MLOps establishes governance frameworks including audit logs for all model deployments, compliance controls, and responsible AI checks. The skill teaches you how to implement fairness assessments, maintain model lineage and reproducibility records, and create audit trails for regulatory requirements. These governance practices ensure your ML systems meet compliance standards and enable transparent, accountable decision-making across your organization.
Can ml-mlops help orchestrate ML pipelines across cloud platforms?
Yes, ML MLOps covers pipeline orchestration strategies across Kubernetes, AWS, GCP, and Azure. The skill guides you through designing automated workflows that handle data validation, model training, evaluation, and deployment. You'll learn how to build portable, repeatable pipelines that work across multiple cloud environments, integrate with CI/CD systems, and scale efficiently while maintaining reproducibility and audit compliance.
SKILL.md
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ML MLOps
Overview
Use this skill for operational machine learning: experiment tracking, reproducibility, orchestration, registries, CI/CD, model deployment governance, monitoring, drift response, and retraining. MLOps turns notebooks and scripts into auditable, repeatable systems with clear ownership and rollback.
MLOps Invariants
Every production ML workflow should answer: - Which data, code, config, environment, and hardware produced this model? - Which metrics, slices, and tests justified promotion? - Where is the
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Related skills
Implementing MLOps equips teams to operationalize machine learning models across the full lifecycle—from experiment tracking and model versioning through feature engineering, deployment, and observability. It covers platform selection for experiment management, feature store implementation, model serving strategies, and pipeline orchestration to help you transition from notebooks to robust, governed production systems.
Master the principles for constructing reliable machine learning systems from data collection through deployment. Learn pipeline design patterns, feature engineering strategies, model validation checklists, and serving approaches—plus monitoring techniques to detect drift and trigger retraining.
ML Pipeline Workflow guides you through constructing production-ready MLOps systems that span data ingestion, preparation, model training, validation, and deployment. It covers pipeline architecture, DAG orchestration patterns, data quality checks, experiment tracking integration, and deployment strategies including canary and blue-green approaches.
Build production ML pipelines that handle data ingestion, model training, and deployment with Airflow DAGs, Kubeflow containers, and MLflow experiment tracking. This skill covers orchestration patterns, scheduling retraining jobs, managing model versions, and troubleshooting common workflow failures.
MLOps Pipelines covers the full lifecycle of production machine learning: from choosing deployment approaches (batch, real-time, edge, streaming) through monitoring model performance and detecting data drift, to implementing CI/CD automation and managing feature stores. Learn model versioning, registry practices, and governance patterns to keep ML systems reliable and reproducible at scale.
This skill guides you through deploying machine learning workloads on managed cloud platforms, Kubernetes clusters, and serverless systems. It covers platform selection across AWS, GCP, Azure, Databricks, and specialized providers, plus practical patterns for endpoint configuration, training job orchestration, and scaling decisions based on your workload's latency, throughput, and compliance needs.
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