Skill UI

This SDK allows developers to build robust anomaly detection applications in Java using Azure AI services. It supports both univariate and multivariate time-series analysis, enabling detection of outliers, trend changes, and correlated signals over large datasets. Ideal for real-time monitoring, operational intelligence, and advanced data quality checks.

This SDK allows developers to build robust anomaly detection applications in Java using Azure AI services. It supports both univariate and multivariate time-series analysis, enabling detection of outliers, trend changes, and correlated signals over large datasets. Ideal for real-time monitoring, operational intelligence, and advanced data quality checks.

A comprehensive Python SDK for accessing Azure AI Language service capabilities. It enables advanced Natural Language Processing (NLP) features, including sentiment analysis, named entity recognition (NER), key phrase extraction, language detection, and sensitive PII detection. Ideal for building sophisticated text analysis applications and data pipelines.

A comprehensive Python SDK for accessing Azure AI Language service capabilities. It enables advanced Natural Language Processing (NLP) features, including sentiment analysis, named entity recognition (NER), key phrase extraction, language detection, and sensitive PII detection. Ideal for building sophisticated text analysis applications and data pipelines.

A comprehensive Python SDK for interacting with the Azure AI Translator service. It provides robust features including real-time text translation, language detection, script transliteration, and detailed dictionary lookups. Ideal for developers building multilingual applications that require accurate cross-cultural text processing.

A comprehensive Python SDK for interacting with the Azure AI Translator service. It provides robust features including real-time text translation, language detection, script transliteration, and detailed dictionary lookups. Ideal for developers building multilingual applications that require accurate cross-cultural text processing.

A comprehensive SDK for building real-time, bidirectional voice AI applications using Azure AI services within the .NET framework. This library facilitates seamless voice interactions, supporting WebSocket communication, advanced features like function calling, and handling audio/text streaming events for developing sophisticated virtual assistants.

The Azure AI VoiceLive SDK for Java enables real-time, bidirectional voice conversations with AI assistants. Using WebSocket technology, this SDK supports low-latency streaming, advanced features like turn detection, noise reduction, and customizable voice selection (OpenAI/Azure). It is ideal for developers building sophisticated voice bots or interactive voice applications in Java.

This SDK enables the development of real-time, conversational AI applications using Azure Cognitive Services. It supports bidirectional communication via WebSockets, allowing for continuous audio streaming (mic input) and receiving synthesized speech output. Ideal for building voice assistants, IVR systems, and interactive voice experiences.

This SDK provides a comprehensive interface for building real-time, bidirectional voice AI applications using Azure AI services. It supports streaming audio input/output via WebSocket, handling transcription, text-to-speech, and complex session management. Ideal for developing robust voice assistants and interactive AI agents in both Node.js and modern browser environments.

Provides expert guidance on designing, implementing, and optimizing cutting-edge computer vision pipelines. Expertise spans real-time object detection (YOLO26), advanced promptable segmentation (SAM 3), and leveraging Vision Language Models (VLMs) for complex visual reasoning. Ideal for building high-performance systems in robotics, autonomous vehicles, and industrial inspection, including 3D reconstruction and edge deployment optimization.

This guide outlines the complete MLOps workflow on Databricks. It covers feature engineering using Feature Store, tracking experiments with MLflow, managing model versions in the Model Registry, and deploying real-time inference endpoints via Mosaic AI Model Serving. Ideal for building robust, production-grade ML pipelines.