Skill UI

This guide provides a deep dive into migrating machine learning workloads, including inference services and training pipelines, from major hyperscalers (AWS, GCP, Azure) to the CoreWeave GPU cloud. It covers cost comparisons, containerization, Kubernetes YAML adaptation, and a phased deployment strategy, helping users ensure a smooth, cost-effective, and optimized transition.

This skill guides developers on implementing Conversational Language Understanding (CLU) using the official azure-ai-language-conversations Python SDK. It demonstrates how to analyze conversation intent and entities, build advanced NLP features, and integrate language understanding into applications using ConversationAnalysisClient. Best practices emphasize using DefaultAzureCredential and context managers for robust, production-ready code.

Build persistent AI agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK, with function tools, hosted tools, streaming, threads, and structured outputs.

Low-level .NET SDK for creating and managing persistent AI agents with threads, messages, runs, and tools. Supports function calling, file search, vector stores, and streaming responses.

Low-level Java SDK for creating and managing persistent Azure AI agents with threads, messages, runs, and tools. Covers installation, authentication, core workflow, best practices, and error handling.

Build AI applications on Microsoft Foundry using the azure-ai-projects SDK. Covers installation, authentication, client operations, agent creation with tools, versioned agents, thread and message flow, connections, deployments, datasets, indexes, evaluations, and async usage.

Build real-time voice AI applications on Azure with bidirectional WebSocket streaming. Covers authentication, session configuration, base64 PCM16 audio streaming, event handling, voice activity detection, and function calling.