Skill UI

This is a high-level Software Development Kit (SDK) designed for managing the full lifecycle of AI projects within Azure AI Foundry. Built for Java, it provides robust client interfaces for core AI operations, including managing connections to Azure resources, uploading and managing datasets, creating and updating search indexes, running model evaluations, and handling deployments. It streamlines complex AI workflow orchestration.

This is a high-level Software Development Kit (SDK) designed for managing the full lifecycle of AI projects within Azure AI Foundry. Built for Java, it provides robust client interfaces for core AI operations, including managing connections to Azure resources, uploading and managing datasets, creating and updating search indexes, running model evaluations, and handling deployments. It streamlines complex AI workflow orchestration.

This SDK provides comprehensive connectivity to Azure AI Search for .NET developers. It enables building sophisticated search applications, supporting full-text search, vector search, semantic understanding, and hybrid query capabilities. Developers can manage indexes, perform document CRUD operations, and implement advanced search patterns like faceted search and autocomplete.

Specializes in provisioning Vertex AI infrastructure with Terraform, covering Model Garden, Gemini endpoints, vector search indexes, ML pipelines, and enterprise-grade production deployments.

This skill facilitates the persistent storage of critical brand knowledge, including campaign learnings, brand guidelines, and competitive intelligence, into a specialized memory layer (such as a vector database). It automatically indexes content, deduplicates entries via hashing, and applies rich metadata tagging, ensuring that valuable institutional knowledge is preserved and retrievable through natural language search across different sessions.

This skill acts as a comprehensive study companion for the seminal Deep Learning book by Goodfellow, Bengio, and Courville. It indexes all 20 chapters, provides reading path planning, and updates 2016-era concepts with a 2016-to-2026 delta. Users can diagnose training failures, understand core frameworks like loss functions and KL divergence, and navigate complex topics like capacity and architecture. It helps keep the knowledge current and turns theoretical concepts into practical decisions for modern AI development.

High-level .NET SDK for Azure AI Foundry projects, enabling agents, connections, datasets, deployments, evaluations, and indexes workflows with authentication and cleanup guidance.

Provides Java SDK guidance for Azure AI Foundry project management, including connections, datasets, indexes, deployments, and evaluations, with authentication and error handling examples.