'workload' 검색 결과

Detect unauthorized cryptocurrency mining in cloud workloads by combining billing changes, CPU usage, mining pool connections, and process alerts. Follow workflows to preserve evidence, isolate affected resources, and trace how attackers gained access.

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• Investigating sudden cloud billing increases
• Responding to GuardDuty cryptocurrency alerts
• Checking containers for unauthorized mining processes

Design and validate rules that limit communication between servers and containers to necessary connections. The skill covers traffic mapping, label-based policies, staged enforcement, and checks for unauthorized movement between systems within a zero trust architecture.

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• Planning communication rules for a zero trust network
• Separating production and development environments
• Restricting access to critical applications

Build dashboards and reports for security operations teams using incident and alert data. Track detection and resolution times, false alarms, analyst workload, and detection coverage to assess operational trends and the effects of improvement initiatives.

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• Preparing monthly or quarterly security operations reports
• Reviewing analyst workload and shift staffing
• Comparing performance before and after tool adoption

Audit Kubernetes access rules to find excessive permissions and service account token abuse paths. Trace which users or workloads could gain cluster administrator access, then produce prioritized findings and recommendations for reducing permissions.

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• Reviewing Kubernetes privilege escalation paths
• Checking permissions after an RBAC change
• Tracing workloads with overprivileged service accounts

Make resume achievements more specific by identifying useful numbers, such as workload, time saved, or revenue. Guided questions uncover measurable contributions, while conservative estimates and documented reasoning help describe experience when exact records are unavailable.

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• Add meaningful numbers to resume achievements
• Find measurable results in everyday work
• Estimate workload when exact records are unavailable

Run machine learning workloads on Modal's cloud GPUs without managing servers. Guides Python configuration for training, inference, batch processing, and API deployment, including automatic scaling, persistent storage, scheduled jobs, and troubleshooting.

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• Running GPU inference without managing servers
• Deploying a model as an automatically scaling API
• Processing large batches in parallel

Build and train Mixture of Experts models, which activate selected specialist networks for each input. Use DeepSpeed or HuggingFace workflows to configure routing, balance workloads, distribute experts across GPUs, and optimize inference.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing MoE models for a limited compute budget
• Implementing Mixtral-style expert routing
• Adjusting expert workloads and training stability

Size support and operations teams using demand ranges and queueing calculations, which estimate how work waits for service. Assess workload imbalance and service target risks, then build a quarterly hiring plan that accounts for training time and attrition.

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• Estimate staffing for support and operations queues
• Check workload imbalance and service target risks
• Build quarterly hiring plans with ramp time and attrition

Manage Linear issues and projects through natural-language requests. Review priorities, create and update tickets, inspect team workloads, and organize release plans using a workflow that reads existing information before making changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prioritize bugs by their impact on users
• Plan issues and assignments for the next sprint
• Review workloads and redistribute assignments

Investigate slow TPU training on GKE by comparing diagnostic events with one-minute monitoring metrics. Separate memory or CPU bottlenecks from hardware and network issues, and identify the appropriate next steps.

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• Investigating slower TPU training without pod crashes
• Checking PERFORMANCE_DEGRADATION events
• Separating resource bottlenecks from infrastructure issues

Diagnoses applications that fail to start or repeatedly crash on GKE, Google's managed Kubernetes service. It examines logs, events, memory use, image pulls, and connectivity, then proposes configuration patches or commands without applying live changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate repeated container restarts
• Diagnose memory exhaustion
• Find causes of image pull or scheduling failures

Guides authentication and access permissions for Google Cloud users and applications. It helps choose credentials for local development, production services, or external clouds, with guidance on short-lived credentials and avoiding downloaded service account keys.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up credentials for local Python development
• Configure Cloud Run access to Cloud SQL
• Connect workloads on AWS or Azure to Google Cloud

Diagnose Google Cloud Filestore mount failures by checking client access rules, network firewalls, and audit logs. Distinguish permission errors from connection timeouts, then provide a structured report and remediation commands that require confirmation before execution.

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• Investigating a Filestore mount that hangs or fails
• Diagnosing access-denied errors from a GKE workload
• Checking client permissions and VPC firewall rules

Configure GKE ComputeClasses, which define preferred computing resources for workloads. Set fallback options between Spot and on-demand VMs, target GPUs or machine families, control class usage, and investigate pods waiting for suitable nodes.

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• Configuring fallback from Spot VMs to on-demand capacity
• Selecting GPUs, TPUs, or machine families
• Restricting ComputeClass editing and workload usage

Deploy AI models on GKE with suitable GPUs or TPUs and model servers. Generate deployment configuration, adjust automatic scaling, and tune inference—the process of producing model responses—for memory use, throughput, and response time.

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• Deploying Gemma or Llama on GKE
• Choosing GPUs or TPUs for model serving
• Configuring automatic scaling for LLM requests

Helps configure persistent storage for GKE applications, including disk selection, storage requests, shared files through Filestore, and volume expansion. It also explains how disk compatibility and zone placement affect workload scheduling.

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• Creating persistent storage for a GKE application
• Sharing files across multiple Pods with Filestore
• Expanding an existing storage volume

Diagnose interrupted or repeatedly restarting AI training jobs managed by JobSet on GKE. Correlate restart metrics, node events, host health, and worker logs to distinguish Spot VM preemptions, infrastructure failures, and application crashes, then recommend responses.

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• Investigating repeated JobSet restarts
• Checking whether Spot VM preemptions interrupted training
• Tracing recurring failures to a physical host

Diagnose GKE nodes marked NotReady or Unknown using node conditions, events, logs, and resource metrics. The skill gathers evidence, distinguishes temporary transitions from faults, and proposes fixes for a person to apply.

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• Investigate persistent NotReady or Unknown node status
• Check why kubelet stopped reporting node status
• Investigate evictions or Pending Pods linked to node health

Choose a Google Cloud database by clarifying data needs, workload, and scale. The skill explains its recommendation and prepares validated Terraform configurations or provisioning scripts for review before infrastructure changes are made.

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• Choosing a database for a new Google Cloud application
• Clarifying data models, workloads, and scale requirements
• Preparing and validating database provisioning code for review

Run model training, fine-tuning, inference, and batch jobs on Modal’s cloud GPUs. Estimate hardware needs and cost, generate Python launch scripts, run or deploy workloads, monitor logs, and retrieve results without managing remote servers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Test CUDA code without a local GPU
• Run model training or LoRA fine-tuning on cloud GPUs
• Deploy a model inference API or vLLM service

Install and configure WizTelemetry Logging in KubeSphere to collect container output and file logs. Use its API to filter logs by workload, time, or keywords, retrieve statistics, and export results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploy log collection to selected clusters
• Enable collection of log files inside containers
• Search logs by keyword, workload, and time

Manage Linear issues and projects through natural-language requests. Review priorities, create and update tickets, inspect team workloads, and organize release plans using a workflow that reads existing information before making changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prioritize bugs by their impact on users
• Plan issues and assignments for the next sprint
• Review workloads and redistribute assignments

Manage isolated working environments in OpenShell using its command-line tool. Register gateways, create and connect to sandboxes, configure credential providers, and reuse workload templates with shared images, environment settings, and computing resources.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create and connect to a sandbox
• Configure and refresh provider credentials
• Inspect and import provider profiles

Train Mixture of Experts models, which activate selected specialist networks for each input. The skill guides architecture, input routing, workload balancing, and GPU distribution using DeepSpeed or HuggingFace, with examples for training configuration and inference optimization.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a model that activates only selected expert networks
• Balancing workloads across expert networks
• Distributing experts across multiple GPUs