'workload' 검색 결과

Analyze sprint history to forecast delivery ranges, score team health, and track retrospective actions. Python tools turn structured sprint data into reports that support planning, highlight recurring problems, and show whether improvement efforts are progressing.

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• Forecasting sprint workload from past delivery data
• Assessing team health and prioritizing improvements
• Tracking retrospective actions and recurring themes

Compares frameworks, libraries, and cloud providers using weighted criteria. Helps teams choose technologies with estimates of five-year total ownership costs, security assessments, community health indicators, and migration effort, risks, and timelines.

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• Comparing frameworks for a new project
• Estimating five-year technology ownership costs
• Assessing migration effort and risks

Diagnose GKE TPU node pool creation or expansion failures despite reserved capacity. Trace which virtual machines and unhealthy node pools occupy reservation slots, check automatic repair settings, and provide remediation steps for the user to apply.

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• Investigate TPU capacity errors despite a reservation
• Identify node pools occupying reserved TPU capacity
• Check automatic repair settings on unhealthy TPU nodes

Diagnose GKE TPU virtual machines that remain in repair by checking instance status, host events, and automatic repair history. Understand the impact on grouped TPU nodes and identify recovery steps for the user to apply.

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• Investigating TPU VMs that remain in REPAIRING
• Assessing how a failed node affects a TPU slice
• Checking automatic repair history and failure reasons

Guides migration of existing codebases to Rust without unsafe code, covering architecture, implementation, testing, and output comparisons. It measures performance before and after optimization and produces an interactive HTML report documenting results and unresolved differences.

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• Migrating a GitHub or local codebase to Rust
• Comparing original and migrated program outputs
• Measuring and optimizing a Rust port

Finds and loads relevant Google skills for choosing or setting up services, databases, storage, authentication, and other platform components. It supplies guidance for comparing Google options, understanding trade-offs, and recognizing when another choice fits better.

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• Compare platforms for running a service
• Find guidance for database or authentication setup
• Locate a skill for a named Google product or API

Design a Google Cloud system for data analysis or machine learning with AI agents. Turn requirements into product choices, an architecture diagram, infrastructure configuration, deployment instructions, and validation steps, with user reviews between phases.

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• Design a data analysis system with multiple AI agents
• Choose Google Cloud products for analytical requirements
• Prepare an architecture diagram and deployment guide

Design and create new Cloud Storage buckets for uses such as backups, media, and AI data. Check project security and name availability, choose storage and protection settings, and produce creation commands or code in the preferred format.

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• Creating storage for application media
• Designing buckets for sensitive data
• Preparing backup and long-term archive storage

Check Google Cloud IAM allow policy changes against the last 90 days of access logs. The skill identifies access that could be lost and applies changes only after a successful simulation and user approval.

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• Checking access impact before removing a role
• Reviewing allow policy changes across resource scopes
• Applying policy changes after simulation and approval

Plan and build AI agents on Google Cloud, from gathering requirements to choosing a single-agent or multi-agent design. Produce architecture diagrams, deployment code and instructions, and validation plans tailored to the workload.

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• Plan an AI agent architecture on Google Cloud
• Choose between single-agent and multi-agent designs
• Prepare deployment code and step-by-step instructions

Reviews Google Cloud spending and resource usage to identify waste, recommend suitable resource sizes, compare discount options, and establish ongoing cost management using the cost optimization guidance in the Google Cloud Well-Architected Framework.

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• Reviewing Google Cloud spending by team or project
• Finding idle resources and oversized instances
• Comparing CUDs, SUDs, and Spot VMs

Review Google Cloud operations and develop guidance for monitoring, incident response, resource sizing, and automated deployments. Use framework-based questions and checklists to identify gaps in production readiness and plan ongoing operational improvements.

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• Reviewing production readiness
• Planning monitoring and logging
• Improving incident response procedures

Create and validate Terraform alerts for GKE using PromQL, a monitoring query language. Cover response times, errors, traffic, memory usage, and cluster health, with checks for alert timing, duplicate signals, and metric ingestion costs.

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• Create alerts for GKE service latency and errors
• Detect frequent container restarts and unhealthy nodes
• Review existing Terraform alert configurations

Create and connect to managed MySQL, PostgreSQL, or SQL Server databases on Google Cloud. This skill guides instance setup, database creation, access permissions, and connection steps, with references for backups, security, and Terraform configuration.

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• Create a PostgreSQL database on Google Cloud
• Connect to a database through Cloud SQL Auth Proxy
• Review database permissions and backup options

Configure and troubleshoot automatic node scaling in GKE. Examine logs and workload constraints to explain why nodes fail to grow or shrink, and guide tuning for capacity shortages, zone placement, and sudden traffic increases.

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• Enable automatic scaling for GKE node pools
• Find why nodes do not scale up
• Identify constraints that prevent nodes from scaling down

Run batch processing, scientific simulations, and machine learning training on Google Kubernetes Engine. Configure parallel jobs, queues that manage resource access, and distributed computation, with guidance on scaling idle capacity down and recovering from interruptions.

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• Run data processing jobs in parallel
• Share computing resources through job queues
• Configure distributed simulations with MPI

Plan and create Google Kubernetes Engine clusters using templates for production, development, or AI workloads. Compare Google-managed and self-managed node options, review configurations before creation, and check settings for production readiness.

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• Create a production Autopilot cluster
• Compare Autopilot and Standard modes
• Prepare a development or test cluster

Plan and configure a shared GKE cluster for multiple teams or environments. Set up namespaces, team permissions, resource limits, and network separation, then use labels and GKE cost allocation to track spending by team.

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• Design a GKE cluster shared by multiple teams
• Separate development, staging, and production workloads
• Set team permissions and resource quotas

Review BigQuery capacity, table layouts, storage retention, and SQL to improve cost efficiency. Compare billing options, plan partitioned tables, and evaluate search indexes, stored aggregation results, and BI Engine for suitable workloads.

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• Compare On-Demand pricing with Editions
• Review storage billing models and retention periods
• Design table partitioning and clustering

Generate Terraform alerts for Cloud Run services, jobs, and worker pools using PromQL, a monitoring query language. Monitor errors, latency, resource usage, job failures, and queue delays, with configurable thresholds and explanations of when each alert triggers.

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• Monitor Cloud Run service errors and latency
• Detect failed batch job executions
• Monitor worker pool backlogs and message age

Investigate failing or slow BigQuery jobs and unexpected cost increases. Compare execution details and resource usage with a normal baseline to isolate bottlenecks, identify likely causes, and determine practical remediation steps.

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• Investigate failed or slow BigQuery jobs
• Locate bottlenecks in execution stages
• Diagnose sudden increases in query or capacity costs

Create reusable Helm charts, packages of Kubernetes deployment templates and settings. The skill guides environment configuration, dependency management, shared templates, and release procedures so applications can be deployed across environments with fewer template changes.

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• Creating reusable Helm charts
• Separating development and production settings
• Managing dependencies and migration order

Organize Jira issues, plan sprints, and define workflows with clear ownership and acceptance criteria. Get help writing JQL, Jira's query language, to find stalled work, review workloads, and support project reporting.

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• Organizing issues and acceptance criteria
• Planning sprints from recent team performance
• Finding stalled issues and blockers with JQL

Create and manage Volcano jobs and scheduling queues in KubeSphere. Use YAML templates for PyTorch, TensorFlow, MPI, and batch workloads, configure resource requests and execution policies, and investigate jobs that remain pending or fail to create pods.

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• Prepare distributed training jobs for PyTorch or TensorFlow
• Create MPI and batch processing jobs
• Configure queue capacity and scheduling weight