Skill UI

This is a high-level SDK for .NET that provides comprehensive client access to Azure AI Foundry services. It enables developers to manage complex AI workflows, including creating and managing conversational agents, handling data connections, uploading datasets, managing model deployments, and setting up search indexes within an Azure AI project structure. Ideal for integrating advanced AI capabilities into enterprise applications.

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 comprehensive SDK provides the .NET framework bindings to manage the Azure API Center service. It enables centralized API inventory management, governance, versioning, and lifecycle control (creation, definition upload, environment deployment). Ideal for building robust, scalable API management pipelines within the Azure ecosystem.

A comprehensive Java SDK client for securely and efficiently sending custom application logs to Azure Monitor. It utilizes Data Collection Rules (DCR) and Data Collection Endpoints (DCE) to manage the ingestion process. The client supports batch uploads, concurrent processing, and robust error handling, making it ideal for integrating custom logging into Azure Log Analytics workspaces.

This SDK provides a dedicated Python interface for securely sending custom logs to Azure Monitor Log Analytics workspace. It utilizes the Logs Ingestion API, supporting features like batch compression, advanced error handling, asynchronous uploads, and compatibility with various authentication models, including sovereign clouds.

A comprehensive guide for building applications that interact with Azure Blob Storage using the Azure SDK for Java. Covers essential patterns including client initialization (via connection string or credentials), uploading/downloading data from files or streams, listing blobs, and managing containers. Ideal for developers building scalable cloud data storage solutions.

This SDK provides a comprehensive Python interface for interacting with Azure Blob Storage, Microsoft's scalable object storage service. It enables users to perform core object storage operations, including uploading, downloading, listing, deleting blobs, managing containers, and setting custom metadata. The library supports both synchronous and asynchronous workflows, making it ideal for building robust data pipelines, data lakes, or cloud-native applications that require reliable, scalable file storage.

This is a comprehensive client library for interacting with Azure Blob Storage, Microsoft's scalable object storage solution for the cloud. It allows developers to perform core operations in Rust, such as uploading, downloading, listing, creating, and managing blobs and containers. Ideal for building cloud-native applications that require persistent, scalable data storage.

This SDK provides a full set of tools for interacting with Azure Blob Storage using JavaScript or TypeScript. It enables developers to perform essential object storage operations, including uploading, downloading, listing, creating, and managing both blobs and containers. Supports various authentication methods like DefaultAzureCredential and Connection Strings, making it ideal for building robust, cloud-native applications.

This SDK provides comprehensive Python support for Azure Data Lake Storage Gen2. It enables developers to interact with hierarchical file systems, performing essential big data operations such as creating, listing, uploading, downloading, and managing metadata for files and directories. Ideal for building data analytics pipelines and cloud-based data processing workflows.

This SDK provides comprehensive Python support for managing Azure File Shares, which function as SMB file shares in the cloud. It allows users to perform core operations including creating, listing, and deleting shares, managing nested directories, uploading, downloading, and copying files, and utilizing advanced features like snapshots and partial range operations. Ideal for cloud-native development and lift-and-shift migrations.