From jeremylongshore-claude-code-plugins-plus-skills
Deploys SageMaker endpoints for ML models with step-by-step guidance, production code/configs, best practices for 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 sagemaker endpoint deployer tasks within the ML Deployment domain.
Generates Jupyter notebook deploying LoRA fine-tuned Nova/OSS models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock.
Deploys trained ML models to production via REST APIs, Docker containers, Kubernetes clusters, with data validation, error handling, and performance monitoring.
Automates Azure ML deployer operations with step-by-step guidance, code generation, and configurations for model serving, MLOps pipelines, monitoring, and production optimization.
Share bugs, ideas, or general feedback.
This skill provides automated assistance for sagemaker endpoint deployer tasks within the ML Deployment domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with sagemaker endpoint deployer" 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