Skill UI

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.

A comprehensive, production-grade reference architecture for integrating with Apollo.io. It details a layered design encompassing API services, business logic, background job processing (e.g., lead enrichment), and data modeling, enabling the building of robust sales intelligence and CRM synchronization systems.

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.

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.