From jeremylongshore-claude-code-plugins-plus-skills
Provides step-by-step guidance and generates configurations for TensorFlow Serving setup in ML deployment, covering model serving, MLOps pipelines, 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 tensorflow serving setup tasks within the ML Deployment domain.
Generates TorchServe configuration files and operations for ML model serving in production. Provides step-by-step guidance, best practices, code, and validation for MLOps pipelines, inference, and monitoring.
Builds production ML systems with PyTorch 2.x, TensorFlow, and modern frameworks for model serving, feature engineering, A/B testing, monitoring, and infrastructure.
Builds production ML systems with PyTorch 2.x, TensorFlow, Hugging Face, and tools for model serving, feature engineering, A/B testing, and monitoring.
Share bugs, ideas, or general feedback.
This skill provides automated assistance for tensorflow serving setup tasks within the ML Deployment domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with tensorflow serving setup" 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