From jeremylongshore-claude-code-plugins-plus-skills
Guides prediction monitoring in ML deployments with step-by-step instructions, best practices, code generation, and configs for MLOps pipelines, model serving, inference, and production 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 prediction monitor tasks within the ML Deployment domain.
Provides guidance for monitoring DataRobot models: tracks performance metrics, detects data/feature/target drift, and identifies prediction anomalies using Python SDK. For production ML health checks.
Assists with model drift detection in ML deployments by providing step-by-step guidance, best practices, production-ready code, and configurations for MLOps monitoring.
Deploys ML models to production serving infrastructure using MLflow, BentoML, Seldon Core with REST/gRPC endpoints, autoscaling, monitoring, A/B testing for scalable real-time inference.
Share bugs, ideas, or general feedback.
This skill provides automated assistance for prediction monitor tasks within the ML Deployment domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with prediction monitor" 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