From jeremylongshore-claude-code-plugins-plus-skills
Executes feature store connector operations and generates configurations for ML deployment including MLOps pipelines, model serving, monitoring, 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 feature store connector tasks within the ML Deployment domain.
Builds a Feast feature store for centralized ML feature management with offline/online stores (Postgres/Redis/BigQuery/DynamoDB), feature views, transformations, and point-in-time joins. Use for training-serving consistency and real-time inference.
Designs production ML systems from data ingestion and feature stores to model training, serving, and monitoring. Use for ML pipelines, MLOps infrastructure, and system design interviews.
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 feature store connector tasks within the ML Deployment domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with feature store connector" 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