Results for “workloads”

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.

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.

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.

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).

Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).

Expert knowledge for Azure Cosmos DB development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Cosmos DB NoSQL/Mongo/Cassandra/PostgreSQL APIs, change feed, vector search, multi-region, or AI/RAG workloads, and other Azure Cosmos DB related development tasks. Not for Azure Table Storage (use azure-table-storage), Azure SQL Database (use azure-sql-database), Azure Database for MySQL (use azure-database-mysql), Azure Database for PostgreSQL (use azure-database-postgresql).