Skill UI

This skill outlines how to implement and manage Role-Based Access Control (RBAC) and namespace isolation for multi-team GPU workloads running on CoreWeave's Kubernetes platform. It provides guidance on setting strict resource quotas, preventing noisy-neighbor issues, and defining granular roles (e.g., ML Engineer, Inference Operator). This is essential for maintaining resource isolation, ensuring compliance (SOC 2, HIPAA), and managing access across large-scale cloud deployments.

This guide provides a deep dive into migrating machine learning workloads, including inference services and training pipelines, from major hyperscalers (AWS, GCP, Azure) to the CoreWeave GPU cloud. It covers cost comparisons, containerization, Kubernetes YAML adaptation, and a phased deployment strategy, helping users ensure a smooth, cost-effective, and optimized transition.

This guide details setting up and managing CoreWeave GPU cloud infrastructure across Development, Staging, and Production environments. It ensures strict resource separation using dedicated Kubernetes namespaces for GPU quotas and scaling policies. Use this when deploying complex ML/AI workloads requiring robust environment isolation and controlled promotion workflows.

This skill provides comprehensive observability for CoreWeave's GPU-intensive workloads running on Kubernetes. It monitors critical operational metrics, including GPU utilization, memory usage, inference latency (p99), and job completion rates. Use it for setting up DCGM metrics dashboards, configuring proactive alerts for underutilization or Out-Of-Memory (OOM) conditions, and ensuring high reliability for AI/ML deployments.

A comprehensive checklist for ensuring GPU workloads, such as inference services or model training pipelines, are fully prepared for production deployment on CoreWeave. It covers critical MLOps and DevOps aspects including autoscaling, resource limits, security policies, persistent storage validation, monitoring setup, and rollback procedures.

This reference architecture provides a comprehensive blueprint for deploying Machine Learning models on CoreWeave's GPU cloud infrastructure. It details the structure for multi-model serving (e.g., vLLM, TGI), scalable Kubernetes deployments, shared persistent storage (PVC), and autoscaling mechanisms using KServe/Knative. Use this when designing robust MLOps pipelines, planning high-performance multi-model inference services, or establishing standardized best practices for GPU cloud deployment.

This SDK provides production-ready Python patterns for managing GPU workloads on CoreWeave. It includes helpers for defining specific GPU affinity, setting resource limits (A100, H100), and generating complete Kubernetes Deployment YAML. Use this library for building robust inference clients, managing GPU deployments programmatically, or automating complex AI workflows.

Provides comprehensive guidelines for securing CoreWeave cloud deployments on Kubernetes. Covers critical security areas including credential management, implementing Role-Based Access Control (RBAC), enforcing network isolation policies, and handling sensitive secrets for ML model access. Essential for hardening GPU workloads and protecting data in cloud environments.

This skill provides comprehensive CI/CD pipeline setup for Fly.io edge deployments. It automates unit testing on pull requests, deploys to staging environments, and promotes changes to production upon merging to the main branch. It handles Docker builds, uses flyctl for deployment, and includes health checks to ensure service reliability and seamless automated delivery.

This skill provides advanced integration for deploying applications on Fly.io, focusing on edge computing architectures. It covers building production-ready Docker images, configuring `fly.toml` for services and health checks, and executing sophisticated deployment strategies like blue-green and canary releases. It ensures reliable, scalable deployment across multiple regions, including automated rollbacks and micro-VM optimization.

This comprehensive skill manages the entire computer vision pipeline, from custom dataset annotation to model deployment. Users can fine-tune YOLO models on proprietary COCO-formatted datasets using an agent-driven workflow. Key features include hardware-aware training (supporting CUDA, MPS, ROCm), automatic export to optimized formats (TensorRT, CoreML, OpenVINO, ONNX), and seamless, one-click deployment as an active detection skill.

This comprehensive tool facilitates the secure deployment of Node.js applications connected to Salesforce across major cloud environments like Heroku, Vercel, and Google Cloud Run. It manages complex authentication using JWT Bearer flow and handles platform-specific secret configuration, ensuring seamless migration of Salesforce-powered applications from sandbox to production.