ml-cloud-deployment
This skill guides you through deploying machine learning workloads on managed cloud platforms, Kubernetes clusters, and serverless systems. It covers platform selection across AWS, GCP, Azure, Databricks, and specialized providers, plus practical patterns for endpoint configuration, training job orchestration, and scaling decisions based on your workload's latency, throughput, and compliance needs.
ml-cloud-deployment helps you deploy and manage ML models across AWS SageMaker, GCP Vertex AI, Azure ML, and other cloud platforms.
AI-generated summary based on this skill's SKILL.md
Install
JosiahSiegel/claude-plugin-marketplace/ml-cloud-deployment · repository language: Shell
git clone https://github.com/JosiahSiegel/claude-plugin-marketplace
cp -r claude-plugin-marketplace/plugins/ml-master/skills/ml-cloud-deployment ~/.claude/skills/ml-cloud-deploymentnpx skillfed install JosiahSiegel/claude-plugin-marketplace/ml-cloud-deploymentFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I deploy a machine learning model to AWS SageMaker?
ml-cloud-deployment guides you through SageMaker model deployment by covering endpoint creation, inference instance selection, and traffic routing. The skill walks you through packaging your model, registering it in the SageMaker Model Registry, and configuring real-time or batch transform jobs. You'll learn to choose between managed endpoints and serverless inference based on your latency and cost requirements.
What are the best practices for scaling ML workloads on GCP Vertex AI?
ml-cloud-deployment covers autoscaling configuration and cost optimization for Vertex AI workloads. The skill explains how to set up custom training jobs with automatic scaling, configure prediction endpoints with traffic-based scaling policies, and use Vertex AI's hyperparameter tuning to optimize resource allocation. You'll learn to balance performance against spending through instance type selection and scaling thresholds.
How do I set up a real-time inference endpoint?
ml-cloud-deployment teaches real-time endpoint setup across AWS, GCP, and Azure platforms. The skill covers containerizing your model, configuring endpoint autoscaling policies, setting up health checks and traffic routing, and monitoring latency and throughput. You'll learn deployment patterns including blue-green deployments and canary rollouts to safely update models in production.
What hardware and infrastructure should I choose for ML training and inference?
ml-cloud-deployment helps you select appropriate compute resources by analyzing your workload's requirements. The skill covers GPU vs. TPU trade-offs, spot instance strategies for cost savings, and infrastructure decisions for batch vs. real-time inference. You'll learn how to profile your model, estimate throughput needs, and match them to cloud provider offerings across AWS, GCP, and Azure.
How can I cost-optimize my ML training jobs on cloud platforms?
ml-cloud-deployment provides strategies for reducing ML training costs across managed platforms. The skill covers using spot instances and preemptible VMs, right-sizing compute resources, scheduling jobs during off-peak hours, and leveraging multi-region deployments. You'll learn to configure autoscaling policies that balance speed against expense and monitor spending through cloud provider cost analysis tools.
What deployment options exist for containerized ML models on Kubernetes?
ml-cloud-deployment covers Kubernetes-based model serving using KServe and similar frameworks. The skill explains containerizing models with Docker, deploying inference servers to Kubernetes clusters, configuring autoscaling based on request volume, and managing model versioning. You'll learn to orchestrate multi-model deployments and implement canary updates for safe production rollouts.
SKILL.md
rendered from the published skill — quoted content, verbatim
ML Cloud Deployment
Overview
Use this skill for deploying ML workloads to managed platforms, Kubernetes, serverless systems, GPU/TPU providers, and lakehouse environments. Start from workload requirements: training or inference, batch or online, latency SLO, throughput, model size, data gravity, compliance, region, hardware, team expertise, and budget.
Platform Selection
| Requirement | Strong choices |
|---|---|
| AWS-native managed lifecycle | SageMaker |
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plugins/ml-master/skills/ml-cloud-deployment/SKILL.md