Results for “workloads”

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.

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.

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.

Assesses and automates upgrades of Azure workloads between plans, tiers, or SKUs, and modernizes Azure SDK for Java dependencies in source code.

Assess Kubernetes workloads and cluster configurations for AKS Automatic compatibility, identify incompatibilities, generate fixes, and guide migration from AKS Standard to AKS Automatic.

Provides 42 technology-agnostic cloud design patterns for distributed systems, covering reliability, performance, messaging, security, and deployment to help architects design robust workloads.

Manages Ray clusters, jobs, Serve deployments, and distributed workloads via the Dashboard API and CLI, with discovery-first checks and safety guardrails.

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.

Provides best practices for using MariaDB's built-in vector support for AI workloads, including SQL syntax for vector columns, indexes, distance functions, and RAG patterns.

IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware...

Evaluates Google Cloud workloads against the Reliability pillar of the Well-Architected Framework, providing actionable recommendations for building, deploying, and managing reliable systems.

Plan and execute Azure deployments safely and repeatably for applications and infrastructure. Use this skill when users ask to deploy, publish, release, or provision workloads in Azure. Covers Infrastructure-as-Code, idempotency, health validation, and rollback strategies.

Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.

Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.

Creates GKE clusters with golden path Autopilot defaults, supporting Standard and GPU workloads. Guides through project, region, and networking inputs, then provisions and verifies cluster settings.

GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, and other RAPIDS libraries for dramatic speedups on numerical, data, ML, graph, and simulation workloads.

Deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads, including enabling protection plans, interpreting findings, and building automated response workflows.

Map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads using Akamai Guardicore Segmentation.

Auto-routes Claude Code subtasks to the cheapest capable model (Haiku/Sonnet/Opus), logs token costs, and A/B tests tiers to validate savings against real workloads.

Evaluates Google Cloud workloads against the Performance Optimization pillar of the Well-Architected Framework, providing actionable recommendations for resource allocation, modular design, elasticity, and monitoring.

Recommends an inference runtime and memory-related launch flags for LLM/VLM workloads on NVIDIA Jetson devices, based on a live memory audit snapshot.

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

Designs and implements a Google Cloud solution for live, bidirectional multimodal streaming workloads with AI agents, covering requirements discovery, architecture design, and deployment planning.

Generates operations-focused guidance for Google Cloud workloads based on the Operational Excellence pillar of the Well-Architected Framework, including assessment questions and validation checklists.

Assess and migrate cross-cloud workloads to Azure with reports and code conversion. Supports Lambda to Functions, Beanstalk/Heroku/App Engine to App Service, Fargate/Kubernetes/Cloud Run/Spring Boot to Container Apps.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Evaluates Google Cloud workloads against the Well-Architected Framework security pillar, identifies security requirements, and provides actionable recommendations for IAM, network security, data protection, and operational security.

IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.

飞书项目（Meego/Meegle）操作工具。支持查询和管理工作项、节点流转、视图查询、个人待办、排期统计等功能。 Use when user needs to work with Feishu/Lark Meego project management — including querying work items, creating/updating work items, completing workflow nodes, checking views, listing todos, analyzing schedules/workloads, or searching with MQL. 关键词：飞书项目、meego、meegle、工作项、需求、任务、缺陷、排期、视图、待办、节点。

Operate, troubleshoot, secure, upgrade, and automate Kubernetes clusters and workloads safely across upstream Kubernetes, k3s, RKE2, MicroK8s, k0s, Talos, OpenShift/OKD, kind, Minikube, Rancher-managed clusters, EKS, AKS, and GKE. Use when a task involves kubectl, Kubernetes APIs, Pods, Deployments, StatefulSets, Services, Ingress or Gateway API, CRDs, RBAC, NetworkPolicy, storage, scheduling, autoscaling, cluster lifecycle, or the bundled agent-first k8s-cli.

Expert knowledge for Azure Sre Agent development including troubleshooting, best practices, decision making, security, configuration, integrations & coding patterns, and deployment. Use when wiring SRE Agent to Azure DevOps/GitHub, Log Analytics/App Insights, AKS Java workloads, or Key Vault, and other Azure Sre Agent related development tasks. Not for Azure Monitor (use azure-monitor), Azure Reliability (use azure-reliability), Azure Resiliency (use azure-resiliency), Azure Service Health (use azure-service-health).

Expert knowledge for Azure Batch development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Batch pools/tasks, autoscale, containers/MPI, storage mounts, or CI/CD for HPC/render workloads, and other Azure Batch related development tasks. Not for Azure HDInsight (use azure-hdinsight), Azure Databricks (use azure-databricks), Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure Virtual Machines (use azure-virtual-machines).

Expert knowledge for Azure Stack Edge development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when deploying IoT Edge modules, Kubernetes/GPU apps, DeepStream pipelines, Arc GitOps, or local ARM workloads, and other Azure Stack Edge related development tasks. Not for Azure Data Box (use azure-data-box-family), Azure IoT Edge (use azure-iot-edge), Azure Local (use azure-local), Azure Virtual Machines (use azure-virtual-machines).

Expert knowledge for Azure Arc development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing Arc-enabled Kubernetes, servers/VMs, data services, resource bridge, or Edge RAG workloads, and other Azure Arc related development tasks. Not for Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure Virtual Machines (use azure-virtual-machines), Azure Stack Edge (use azure-stack-edge), Azure VMware Solution (use azure-vmware-solution).

Expert knowledge for Azure Elastic SAN development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when creating iSCSI volumes, AVS datastores, snapshots, CMK encryption, or AKS-integrated workloads, and other Azure Elastic SAN related development tasks. Not for Azure NetApp Files (use azure-netapp-files), Azure Managed Lustre (use azure-managed-lustre), Azure Container Storage (use azure-container-storage), Azure Virtual Machines (use azure-virtual-machines).