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Found 4 Skills
Configure autoscaling for Kubernetes, VMs, and serverless workloads based on metrics, schedules, and custom indicators.
Implement Customer.io load testing and scaling. Use when preparing for high traffic, load testing, or scaling integrations for enterprise workloads. Trigger with phrases like "customer.io load test", "customer.io scale", "customer.io high volume", "customer.io performance test".
Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for generic batch jobs or HPC task queues (use gke-batch-hpc instead).
Manages scaling for GKE workloads using HPA and VPA. Use when configuring Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA), or applying best practices for GKE workload autoscaling. Do not use for cluster-level autoscaling (Cluster Autoscaler), static cluster sizing, or configuring node-level machine styles directly.