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home / skills / jeremylongshore / claude-code-plugins-plus-skills / sagemaker-endpoint-deployer

sagemaker-endpoint-deployer skill

/skills/08-ml-deployment/sagemaker-endpoint-deployer

This skill automates deploying SageMaker endpoints with production-ready configurations, best practices, and validation to streamline ML deployment workflows.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill sagemaker-endpoint-deployer

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: "sagemaker-endpoint-deployer"
description: |
  Deploy sagemaker endpoint deployer operations. Auto-activating skill for ML Deployment.
  Triggers on: sagemaker endpoint deployer, sagemaker endpoint deployer
  Part of the ML Deployment skill category. Use when deploying applications or services. Trigger with phrases like "sagemaker endpoint deployer", "sagemaker deployer", "deploy sagemaker endpoint er".
allowed-tools: "Read, Write, Edit, Bash(cmd:*), Grep"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Sagemaker Endpoint Deployer

## Overview

This skill provides automated assistance for sagemaker endpoint deployer tasks within the ML Deployment domain.

## When to Use

This skill activates automatically when you:
- Mention "sagemaker endpoint deployer" in your request
- Ask about sagemaker endpoint deployer patterns or best practices
- Need help with machine learning deployment skills covering model serving, mlops pipelines, monitoring, and production optimization.

## Instructions

1. Provides step-by-step guidance for sagemaker endpoint deployer
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards

## Examples

**Example: Basic Usage**
Request: "Help me with sagemaker endpoint deployer"
Result: Provides step-by-step guidance and generates appropriate configurations


## Prerequisites

- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml deployment concepts


## Output

- Generated configurations and code
- Best practice recommendations
- Validation results


## Error Handling

| 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 |


## Resources

- Official documentation for related tools
- Best practices guides
- Community examples and tutorials

## Related Skills

Part of the **ML Deployment** skill category.
Tags: mlops, serving, inference, monitoring, production

Overview

This skill automates SageMaker endpoint deployment tasks to help you serve models in production reliably and quickly. It provides step-by-step guidance, generates deployment code and configuration, and validates outputs against common standards. Use it to streamline ML deployment workflows and reduce manual errors.

How this skill works

The skill inspects deployment requirements such as model artifacts, container images, instance types, and IAM permissions, then produces Terraform/CloudFormation or SageMaker SDK code snippets to create endpoints. It follows industry best practices for production readiness: autoscaling, model validation, logging, and monitoring hooks. It can also surface common configuration issues and suggest fixes before execution.

When to use it

  • You need to deploy a trained model to a SageMaker real-time or multi-model endpoint
  • You want production-ready endpoint configurations with autoscaling and monitoring included
  • You need code samples for SageMaker SDK, container setup, or infrastructure as code
  • You want to validate IAM roles, instance choices, or network settings before deployment
  • You’re building repeatable ML deployment pipelines or CI/CD for model serving

Best practices

  • Use production-ready container images and pin runtime versions to ensure reproducibility
  • Configure autoscaling and request tracing to handle variable traffic and diagnose issues
  • Attach least-privilege IAM roles and validate permissions prior to deployment
  • Enable model validation and canary releases for iterative rollout with rollback capability
  • Integrate CloudWatch or Prometheus metrics and centralized logging for observability

Example use cases

  • Generate SageMaker Python SDK script to create a real-time endpoint with autoscaling rules
  • Produce CloudFormation or Terraform snippets for endpoint, endpoint config, and IAM roles
  • Recommend instance types and concurrency settings for cost-performance balance
  • Create a canary rollout plan and rollback steps for a risky model change
  • Validate endpoint configuration and report missing permissions or misconfigured network settings

FAQ

What inputs do I need to provide for a deployment?

Provide model artifact location (S3), container image URI, desired instance type/count, IAM role ARN, and any VPC/subnet settings if using a private endpoint.

Can this generate both SDK code and IaC templates?

Yes. The skill can produce SageMaker SDK scripts as well as CloudFormation or Terraform snippets to support different deployment workflows.

How does it handle scaling and monitoring?

It recommends and generates autoscaling policies, exposes metrics for latency/error rates, and wires logs/metrics to CloudWatch or a specified monitoring stack.