Skill UI

Automates creation of A/B test configurations for ML deployment, guiding users through best practices, code samples, and validation steps so production models are served with monitoring and rollout control.

Automates adapting pretrained models via transfer learning by analyzing requirements, generating PyTorch or TensorFlow fine-tuning code, validating data, tracking KPIs, and saving artifacts so you can quickly reuse existing models for new datasets and tasks.

This comprehensive guide details the architecture of memory for intelligent agents. It covers the distinction between short-term context, long-term vector storage, and advanced cognitive models (CoALA framework: semantic, episodic, procedural). Learn how to implement robust memory retrieval mechanisms, select appropriate vector databases (Pinecone, Qdrant, etc.), and utilize embedding models to build truly context-aware, persistent AI applications.

This skill provides optimal best practices and patterns for building ViewModels, Wizards, and managing complex navigation flows in Avalonia applications. It leverages the power of ReactiveUI for functional state management and Zafiro for structured command handling, ensuring the creation of robust, maintainable, and scalable MVVM architectures.

Activation-aware weight quantization that compresses 7B-70B models to 4-bit with minimal accuracy loss, delivering ~3x inference speedups and memory reductions for constrained GPU deployments while integrating with vLLM and Marlin kernels.

A comprehensive SDK for the Azure AI Document Intelligence service, designed for .NET developers. This tool allows you to programmatically extract text, structured data, and tables from various document types, including invoices, receipts, ID cards, and general documents. It supports both powerful prebuilt models and custom model training.

A comprehensive SDK for the Azure AI Document Intelligence service, designed for .NET developers. This tool allows you to programmatically extract text, structured data, and tables from various document types, including invoices, receipts, ID cards, and general documents. It supports both powerful prebuilt models and custom model training.

A comprehensive TypeScript client for the Azure Document Intelligence REST API. This SDK allows developers to programmatically extract structured data, text, and tables from various document types, including PDFs and images. It supports using prebuilt models (like invoices, receipts, and IDs) and building custom models for specialized document processing workflows.

A comprehensive TypeScript client for the Azure Document Intelligence REST API. This SDK allows developers to programmatically extract structured data, text, and tables from various document types, including PDFs and images. It supports using prebuilt models (like invoices, receipts, and IDs) and building custom models for specialized document processing workflows.

This comprehensive Python SDK serves as a client library for managing the full lifecycle of Machine Learning resources on Azure Machine Learning. It allows users to programmatically handle workspaces, register data and models, provision compute clusters, define complex training jobs, and build reproducible, end-to-end MLOps pipelines.

This comprehensive Python SDK serves as a client library for managing the full lifecycle of Machine Learning resources on Azure Machine Learning. It allows users to programmatically handle workspaces, register data and models, provision compute clusters, define complex training jobs, and build reproducible, end-to-end MLOps pipelines.

The official client library for interacting with Azure OpenAI services in .NET. It provides comprehensive access to various OpenAI models, including GPT-4o, embeddings, image generation (DALL-E), and audio transcription (Whisper). Use cases include building advanced chat applications, generating structured JSON outputs, and implementing Retrieval-Augmented Generation (RAG).