aws-sagemaker
Amazon SageMaker for building, training, and deploying machine learning models. Use for SageMaker AI endpoints, model training, inference, MLOps, and AWS machine learning services.
npx skills add majiayu000/claude-skill-registry --skill aws-sagemaker --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# AWS SageMaker Skill Comprehensive assistance with Amazon SageMaker development, covering the complete ML lifecycle from data preparation to model deployment and monitoring. ## When to Use This Skill This skill should be triggered when: **Model Training & Development** - Training ML models on SageMaker infrastructure - Using SageMaker training jobs or HyperPod clusters - Implementing distributed training workflows - Building custom training containers **Model Deployment & Inference** - Deploying models to real-time endpoints - Setting up serverless inference endpoints - Configuring batch transform jobs - Managing endpoint auto-scaling - Deploying models with Inference Recommender **Data Preparation** - Working with SageMaker Data Wrangler - Preparing datasets for training - Implementing data transformation pipelines **Model Management & MLOps** - Registering models in Model Registry - Managing model versions and lifecycle - Setting up model monitoring with Model Monitor - Tracking model quality, bias, and drift - Implementing CI/CD for ML workflows **SageMaker Studio & Environments** - Setting up SageMaker domains and user profiles - Configuring Studio environments - Working with
- When to Use This Skill
- Key Concepts
- Quick Reference
- Example 1: List Recent Monitoring Executions
- Example 2: AWS CLI Configuration for SageMaker
- Example 3: Data Wrangler URLs for Firewall Configuration
- Example 4: Create Model with ModelBuilder
- Example 5: Model Registry ARN Pattern
- Example 6: AWS Marketplace Subscription Management
- Example 7: Serverless Endpoint Monitoring Metrics
- Example 8: Model Package Resource Groups
- Example 9: Processing Job Environment Variables
- Example 10: Model Monitoring Violations Report
- Reference Files
Configure AWS credentials aws configure This will prompt for:
What does the aws-sagemaker skill do?
Amazon SageMaker for building, training, and deploying machine learning models. Use for SageMaker AI endpoints, model training, inference, MLOps, and AWS machine learning services.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill aws-sagemaker --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.