implementing-mlops
De la autor
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
Instalare
npx skills add ancoleman/ai-design-components --skill implementing-mlops -a claude-code
Se instalează în .claude/skills/implementing-mlops/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
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- Include scripturi pe care nu le-am verificatScăzut
De ce 73
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Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- ml-pipelineJeffallan/claude-skillsDesigns and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.DevOps★ 12.1K
- mlops-engineertranhieutt/software_development_departmentProvides MLOps patterns for ML CI/CD pipelines, model registries, monitoring, and data drift detection. Use when setting up ML infrastructure or when the user mentions MLOps, model deployment, ML pipeline, or model monitoring.DevOps★ 74
- ml-pipeline-creationseb1n/awesome-ai-agent-skillsDesign, implement, and validate reproducible machine-learning pipelines spanning data preparation, training, evaluation, registry, and deployment gates. Use when the user requests an ML pipeline, needs to turn model scripts into an orchestrated workflow, or provides pipeline components that must be connected safely.DevOps★ 229
- mlflowOrchestra-Research/AI-Research-SKILLsTrack ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platformDevOps★ 13.9K
- ml-pipeline-workflowwshobson/agentsBuild end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.Productivitate★ 41.7K
- model-deploymentsecondsky/claude-skillsDeploy ML models with FastAPI, Docker, Kubernetes. Use for serving predictions, containerization, monitoring, drift detection, or encountering latency issues, health check failures, version conflicts.DevOps★ 236
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