Results for “workloads”

Configures GKE storage including PVCs, PersistentVolumes, Filestore, and GCS FUSE with best practices for production workloads.

Diagnose and root-cause issues with OCI Kubernetes Engine clusters and workloads through evidence-driven investigation.

Protects stateful GKE workloads by configuring backup plans, restore workflows, and disaster recovery using Backup for GKE.

Provides production-grade Kubernetes YAML patterns and kubectl debugging commands for deploying, managing, and debugging workloads reliably.

Operate long-lived agent workloads with observability, security boundaries, and lifecycle management.

Manage AWS EKS clusters, node groups, IRSA, add-ons, and workloads using AWS CLI and eksctl commands.

Deploy and scale Python AI/ML workloads on Modal's serverless cloud, including GPU compute, web endpoints, scheduled jobs, and persistent storage.

Optimize GKE costs by rightsizing workloads, configuring Spot VMs, selecting machine types, and using Committed Use Discounts.

Create and manage Kubernetes workloads with declarative YAML manifests, covering deployments, networking, security, storage, Helm charts, and troubleshooting.

Set up and manage Google Kubernetes Engine clusters, node pools, workloads, networking, and storage with Autopilot defaults.

Provides expertise in containerization technology, covering container creation, orchestration integration, security hardening, and operational best practices for production workloads.

Validate enterprise workloads against Google Cloud best practices using public client libraries and the REST API to manage evaluations, rules, scanned resources, and validation results.

Set up GPU profiling, tracing, and monitoring for inference workloads using vLLM, SGLang, PyTorch, and dstack services via the Graphsignal Profiler sidecar.

Evaluates a containerized framework for deploying distributed big data workloads, measuring execution time and cloud cost scaling from four to eight nodes.

Monitors cloud workloads for runtime threats by checking process lists, network connections, file integrity, and resource utilization anomalies on EC2 and GCE instances.

Evaluates Google Cloud workloads against the Sustainability pillar of the Well-Architected Framework, providing actionable recommendations to minimize environmental impact.

Routes user requests to specialist skills for AEM 6.5 LTS workflow model design, custom step development, launcher configuration, triggering, debugging, and triaging.

Debug AEM Workflow issues on AEM 6.5 LTS and AMS including stuck workflows, failed steps, missing Inbox tasks, launcher failures, stale instances, thread pool exhaustion, queue backlogs, purge failures, and permissions errors.

`task-agent`/`review-agent`: use when a Kubernetes workload, route, identity, health, capacity, or rollout boundary changes; skip image-only, pipeline-only, and non-cluster work.

Design, build, and manage AWS Step Functions state machines for serverless workflow orchestration, including error handling, parallel execution, and integration with AWS services.

Ship workflow: detect + merge base branch, run tests, review diff, bump VERSION, update CHANGELOG, commit, push, create PR. Use when asked to "ship", "deploy", "push to main", "create a PR", "merge and push", or "get it deployed". Proactively invoke this skill (do NOT push/PR directly) when the user says code is ready, asks about deploying, wants to push code up, or asks to create a PR. (gstack)

Configure and deploy Workflow Launchers that automatically start workflows in response to JCR content changes on AEM 6.5 LTS.

Write and validate EAS CI/CD workflow YAML files for Expo projects, using the official schema and documentation.

Run ML workloads on Modal's serverless GPU cloud platform with auto-scaling, pay-per-second pricing, and Python-native infrastructure.

Inngest expert for serverless-first background jobs, event-driven workflows, and durable execution without managing queues or workers.

Build serverless background jobs, event-driven workflows, and durable execution with Inngest, without managing queues or workers.

Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.

Land and deploy workflow. Merges the PR, waits for CI and deploy, verifies production health via canary checks. Takes over after /ship creates the PR. Use when: "merge", "land", "deploy", "merge and verify", "land it", "ship it to production". (gstack)

Deploy and manage Cloudflare Workers for Platforms, including dispatch namespaces, static assets, and outbound worker configuration.

Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.