Skill UI

This Python SDK enables management of Azure Machine Learning resources including workspaces, jobs, models, datasets, compute clusters, and pipelines. It supports authentication, environment configuration, and best practices for reproducible ML workflows on Azure.

Deploy and manage production-grade Kubernetes clusters on Azure using AKS. Covers cluster creation, node pool configuration, networking, ingress controllers, monitoring, and security. Includes Terraform provisioning and best practices for microservices orchestration with auto-scaling and Azure AD integration.

Learn to operate GPU-backed Kubernetes clusters for AI inference and training workloads. Covers NVIDIA GPU Operator installation, device plugin configuration, MIG partitioning for resource sharing, and time-slicing for non-MIG GPUs. Includes GPU-aware autoscaling, health monitoring with DCGM and Prometheus, and troubleshooting scheduling or driver issues. Essential for production AI infrastructure management.

This skill covers production patterns for Linkerd, a lightweight security-first service mesh for Kubernetes. It includes setup instructions, namespace injection, service profiles for retries/timeouts, traffic splits for canary deployments, server authorization policies, and multi-cluster configurations. Ideal for DevOps engineers managing microservices security and traffic management.

This skill enables horizontal scaling of Large Language Model inference clusters on Kubernetes. It utilizes KEDA for event-driven autoscaling based on custom GPU metrics like request queues and cache usage. The guide covers deploying vLLM on GPU nodes, configuring Prometheus for monitoring, implementing spot instance strategies for cost efficiency, and setting up queue-based scaling for batch jobs. It ensures optimal resource utilization and cost management for high-traffic AI workloads.

Manage and deploy applications on Red Hat OpenShift Container Platform. This skill covers cluster administration, project management, RBAC, and deployment strategies including S2I builds and Operators. Users will learn to use the oc CLI for authentication, creating routes, managing image streams, and configuring deployment configs for containerized workloads in enterprise environments.

Configure and optimize Redis for high-performance caching, data storage, and real-time processing. This skill covers installation, security hardening, persistence strategies (RDB/AOF), and high availability setups like Sentinel and Cluster. Ideal for reducing database load, implementing queues, and managing session storage in production environments.

This skill provides expert-level assistance in designing and managing service mesh architectures using tools like Istio and Linkerd. It covers traffic management, security policies, observability, and multi-cluster configurations. Ideal for implementing zero-trust networking, canary deployments, and debugging microservices communication in Kubernetes environments.

Manage SSL/TLS certificates for web servers, Kubernetes clusters, and internal services. Covers Let's Encrypt with Certbot, automated renewal, cert-manager deployment, and configuration of ClusterIssuers and Certificate resources.