Skill UI

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 workflow guides users through the complete lifecycle of customizing large language models (LLMs). It covers uploading training data, initiating fine-tuning jobs, monitoring training progress, managing model versions, and finally deploying the optimized model to a dedicated, autoscaling inference endpoint using the Together AI API. Ideal for production-level customization.

Reduce AWS spend using rightsizing, autoscaling, Savings Plans, budgets, anomaly detection, and storage lifecycle policies. Supports FinOps reviews, cloud bill reduction, and cost-per-request optimization.

Learn to operate GPU-backed Kubernetes clusters for AI inference and training workloads. Covers NVIDIA GPU Operator installation, device plugin configuration, MIG partitioning for resource sharing, and time-slicing for non-MIG GPUs. Includes GPU-aware autoscaling, health monitoring with DCGM and Prometheus, and troubleshooting scheduling or driver issues. Essential for production AI infrastructure management.

This skill enables horizontal scaling of Large Language Model inference clusters on Kubernetes. It utilizes KEDA for event-driven autoscaling based on custom GPU metrics like request queues and cache usage. The guide covers deploying vLLM on GPU nodes, configuring Prometheus for monitoring, implementing spot instance strategies for cost efficiency, and setting up queue-based scaling for batch jobs. It ensures optimal resource utilization and cost management for high-traffic AI workloads.

Deploy ML models on Kubernetes with KServe and NVIDIA Triton Inference Server. Covers canary deployments, autoscaling, GPU-aware scheduling, and traffic splitting for production inference workloads.

Comprehensive guides and best practices for working with Neon Serverless Postgres. Covers setup, connection methods, branching, autoscaling, scale-to-zero, read replicas, connection pooling, Neon Auth, CLI, MCP server, REST API, and SDKs for TypeScript and Python. Ideal for developers implementing serverless database solutions.

Provides guidance on using Neon, a serverless Postgres platform with autoscaling, branching, instant restore, and scale-to-zero. Covers setup, connection methods, features, drivers, ORMs, auth, platform API, and CLI integration.