From jeremylongshore-claude-code-plugins-plus-skills
Manages model registry operations for ML deployment, providing step-by-step guidance, production-ready code, and configurations for model serving, MLOps pipelines, monitoring, and optimization.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin langchain-py-packThis skill is limited to using the following tools:
This skill provides automated assistance for model registry manager tasks within the ML Deployment domain.
Provides step-by-step guidance, code, and configurations for model versioning manager tasks in ML deployment, covering MLOps pipelines, serving, inference, monitoring, and production optimization.
Registers trained models in MLflow Model Registry with versioning, stage transitions (Staging, Production, Archived), approval workflows, and lineage tracking. Use for promoting models to production, governance, rollback, and compliance.
Builds ML pipelines, tracks experiments, and manages model registries with MLflow, Kubeflow, Airflow, SageMaker, and Azure ML. Automates training, deployment, monitoring for MLOps infrastructure.
Share bugs, ideas, or general feedback.
This skill provides automated assistance for model registry manager tasks within the ML Deployment domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with model registry manager" Result: Provides step-by-step guidance and generates appropriate configurations
| Error | Cause | Solution |
|---|---|---|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |
Part of the ML Deployment skill category. Tags: mlops, serving, inference, monitoring, production