AI Learning Directory

Amazon SageMaker Unified Studio is a cutting-edge all-in-one development environment that integrates data analytics and machine learning workflows, addressing the need to unify disparate tasks in modern enterprises. This article explores the technical architecture, core capabilities, and governance features of SageMaker Unified Studio, offering valuable insights for AI developers and builders.

Google Agent Space, a platform by Google Cloud, empowers enterprises to create, deploy, and manage AI agents leveraging Google's advanced AI technologies. This article highlights how Agent Space facilitates seamless integration with popular apps, enables multimodal search across enterprise data, drives impact through content generation and automation, and unlocks innovation with AI agents, emphasizing enterprise-grade security and compliance.

This article explores the evolving use cases of AI, particularly in personal and professional lives, focusing on applications like therapy/companionship, organizing life, and finding purpose. It delves into the surprising shift from productivity-driven uses to more growth-oriented and personal applications, shedding light on the human-AI interaction landscape.

Google Cloud's Agentspace is a powerful Enterprise Search and Assistant platform that utilizes intelligent agents powered by Gemini reasoning to enhance productivity by tackling information silos. This article provides an overview and UI walkthrough of Agentspace, offering insights into how it can transform enterprise information access and boost productivity, making it valuable for AI developers interested in building AI assistants and enterprise search solutions.

This article delves into the systematic process of identifying and prioritizing high-impact AI use cases for enterprise implementation, covering strategic imperatives, alignment with core strategies, measuring business value, feasibility, data readiness, risk management, ROI, scalability, ethical considerations, talent and skills, sustainability, adoption, and customer impact. It provides a comprehensive blueprint for leaders navigating AI adoption in organizations.

This article delves into the use cases of AI, focusing on image/media generation and AI customer support chat systems. The author shares insights from their AI deep dive, including building AI apps like chatbots on Telegram to showcase AI applications. AI developers can learn about practical AI implementations and the potential of AI in enhancing existing products.

This article provides insights for Product Managers (PMs) working on AI features, emphasizing the importance of understanding user needs and business value. It highlights the practical approach to learning AI through user-centric lenses and the significance of asking questions when faced with new challenges.

This article from MIT Sloan Teaching & Learning Technologies provides beginner-friendly resources on key AI concepts like neural networks, natural language processing, and model architectures. It offers insights into generative AI foundations for teaching and practical tips on writing effective prompts and mitigating bias in AI tools.

This article clarifies the distinction between AI and machine learning, highlighting how machine learning is a subset of AI that focuses on training algorithms to learn from data. It provides insights into popular terms like neural networks, deep learning, and large language models, offering a foundational understanding of how these concepts interrelate.

Azure AI Foundry offers a comprehensive platform for designing, customizing, and managing AI applications and agents using various tools like GitHub, Visual Studio, and Copilot Studio. Developers can access a wide range of industry-leading AI models and services to build and deploy AI solutions efficiently.

This beginner's guide from Coursera provides a structured approach to learning artificial intelligence, emphasizing the importance of creating a learning plan, mastering prerequisite skills, and starting with AI fundamentals. It highlights the wide-ranging applications of AI in everyday life and the potential career opportunities in the field.

Agentic AI is an advanced artificial intelligence system that exhibits autonomy, goal-driven behavior, and adaptability, allowing it to accomplish specific tasks independently. This article explores how agentic AI leverages generative AI techniques and large language models to function in dynamic environments, highlighting its potential for autonomous completion of complex tasks.

This tutorial guides AI developers in building a retrieval agent that can make decisions on retrieving context from a vector store or responding directly to users. Key takeaways include fetching and preprocessing documents, indexing them for semantic search, and creating an agentic RAG system for decision-making.

The article explores the distinction between Agentic AI, focusing on autonomous decision-making, and Generative AI, which creates new content like text, images, and code. It discusses the impact of large language models, machine learning, and natural language processing on autonomous tasks, highlighting examples like ChatGPT and autonomous vehicles.

Azure AI Foundry is introduced as a comprehensive solution for creating AI agents efficiently. The platform offers the ability to select appropriate models and enhance agents with knowledge, providing valuable insights for AI developers looking to build intelligent solutions.

Graph RAG is a powerful approach that enhances large language models (LLMs) with external knowledge, enabling more relevant and accurate answers to natural language questions. This article delves into the importance of integrating domain-specific proprietary knowledge into conversational interfaces through the Graph RAG approach, offering valuable insights for AI developers looking to optimize question-answering systems.

Agentic RAG leverages AI agents to enhance retrieval augmented generation systems, enabling large language models to retrieve information from multiple sources and handle complex workflows. This article delves into the concept of RAG, its components, and the benefits of incorporating AI agents for improved accuracy and adaptability in AI models.

GraphRAG introduces a structured, hierarchical approach to Retrieval Augmented Generation (RAG), enhancing language models' reasoning abilities by leveraging knowledge graphs. This article delves into the process of extracting knowledge graphs from text, building community hierarchies, and generating summaries for improved question-and-answer performance.

This Harvard lecture delves into the AI primitives transforming content creation, covering spatial intelligence, visual intelligence, hybrid workflows, content paradigms, and the future of media. It offers insights into cutting-edge AI technologies and their applications in creative industries, providing a blueprint for creators and innovators.

This code snippet demonstrates how to use agent operations with the Sharepoint tool from the Azure Agents service using a synchronous client. It creates an agent, initializes a Sharepoint tool with a connection ID, and interacts with agents and threads for communication. The code provides a practical implementation of agent orchestration and tool integration within an AI project environment.

This code snippet demonstrates how to use agent operations with a toolset from the Azure Agents service using a synchronous client. It showcases creating an agent with a toolset, enabling auto function calls, creating threads for communication, and sending messages within an agent system.

This code snippet demonstrates how to use agent operations with the OpenAPI tool from the Azure Agents service using custom key authentication against the TripAdvisor API. It showcases setting up connections, loading OpenAPI specs, and initializing an Agent OpenAPI tool. This code provides practical implementation examples for integrating Azure AI services with external APIs.

This code snippet demonstrates how to use the Azure Agents service with OpenAPI tools for agent operations using a synchronous client. It showcases setting up authentication, initializing agent tools with OpenAPI specs, and creating an agent to retrieve weather information and a list of countries. The code provides hands-on experience in integrating Azure AI services and working with OpenAPI specifications.

This code snippet demonstrates how to use agent operations with multiple Model Context Protocol (MCP) servers from the Azure Agents service using a synchronous client. It showcases how to interact with AI models deployed on different MCP servers and manage tools dynamically within the agents.

This code snippet demonstrates how to use Azure Agents service with the Connected Agent tool in Python. It shows how to create multiple agents, such as 'stock_price_bot' and 'weather_bot', and provides instructions for setting up the environment variables and dependencies before running the sample.

This code snippet demonstrates how to use agent operations with the Model Context Protocol (MCP) tool from the Azure Agents service using a synchronous client. It provides a practical example of creating an agent with MCP tool and processing agent runs within an Azure AI project environment.

This code snippet demonstrates how to use agents with Logic Apps to execute the task of sending an email. It provides a sample implementation using Azure AI agents and Logic Apps, along with instructions on prerequisites and usage.

This code snippet demonstrates how to use agent operations with custom functions from the Azure Agents service using a synchronous client. It showcases creating an agent, running user requests with function calls, creating threads, messages, and runs, and monitoring the status of the run.

This code snippet demonstrates how to use agent operations with file searching from the Azure Agents service using a synchronous client. It showcases uploading a file, creating a vector store, and creating an agent with a file search tool, providing hands-on experience with Azure AI Projects and Agents SDK.

This code snippet demonstrates how to use Agent operations with the Microsoft Fabric grounding tool from the Azure Agents service using a synchronous client. It showcases creating an agent, initializing a Fabric tool, creating threads for communication, and processing an Agent run with tools.

This code snippet demonstrates how to add files to an agent during the vector store creation using Azure AI tools. It involves setting up the necessary environment variables, creating a vector store, and utilizing Azure services for data storage and retrieval.

This code snippet demonstrates how to use Azure Agents service with the Deep Research tool through a Python client. It includes functions for converting citation markers in markdown content to HTML superscript format. The sample provides practical guidance on integrating Azure AI services and processing text data.

This code snippet demonstrates how to use the Connected Agent tool from the Azure Agents service to create agents for specific tasks like getting stock prices. It showcases the integration of AI models and agents for building conversational AI systems.

This code snippet demonstrates how to use agent operations with a code interpreter from the Azure Agents service using a synchronous client. It showcases creating an agent, creating a thread, and uploading a local file to Azure for vector store creation.

This code snippet demonstrates how to use agent operations with a code interpreter from the Azure Agents service using a synchronous client. It showcases uploading a file, creating an agent with a code interpreter tool, and creating a message thread within an AI project environment.

This code snippet demonstrates how to use the Azure Agents service with the Browser Automation tool for browser automation tasks. It shows how to create a new agent with the Browser Automation tool attached, enabling the agent to perform tasks related to web browsing.

This code snippet demonstrates how to create an agent using the Bing grounding tool from the Azure Agents service. It showcases the integration of the Azure AI Project client, agent creation with specific tools, and message handling within a thread, providing insights into building AI agents with specialized functionalities.

This code snippet demonstrates how to use the Azure Agents service with the Bing Custom Search tool to create an agent for performing search operations. It showcases the integration of Azure AI tools for agent orchestration and communication.

This code snippet demonstrates how to use Azure Function agent operations from the Azure Agents service using a synchronous client. It showcases setting up an Azure Function tool to interact with a bot and provides guidance on required environment variables and dependencies. The learning value includes understanding how to integrate Azure Functions with AI agents for automated tasks.

This code snippet demonstrates how to use agent operations with Azure AI Search tool from the Azure agents service using a synchronous client. It provides a practical example of setting up an agent with Azure AI Search capabilities and initializing the AzureAISearchTool for performing search operations.

This code snippet demonstrates how to enable automatic function calls in Azure AI agents using Python. It shows how to create an agent, set up functions to be called automatically, and initialize the agent for use in AI projects. The code provides a practical example for integrating AI agents with custom functions.

The article explores the future impact of AI on various industries, highlighting its role in data analysis, research, human care, household tasks, workplace efficiency, and safety. It discusses the increasing adoption of AI by enterprises and the influence of generative AI tools like ChatGPT, providing insights into the evolving landscape of artificial intelligence.

The MIT report highlights a concerning 95% failure rate of generative AI pilots in companies, emphasizing the challenges in implementing enterprise AI solutions. This article sheds light on the GenAI Divide, offering valuable insights into the core issues hindering the success of AI initiatives in organizations.

This article provides a step-by-step guide for .NET developers to create a modular multi-agent system using Google's A2A, Microsoft's Semantic Kernel, and Semantic Kernel C# SDK. Readers will learn about the architecture of multi-agent systems, setting up A2A servers and clients, and real-time orchestration, offering practical insights into building AI agents that collaborate seamlessly.

The blog post introduces the concept of agentic AI in Azure AI Foundry, emphasizing how agents can reason, act, and collaborate to bridge knowledge and outcomes. It discusses common use cases and design patterns for leveraging agentic AI, providing valuable insights for AI developers interested in building intelligent agents.

This article introduces the Model Context Protocol (MCP) and its significance for Azure AI Foundry, enabling seamless integration between AI agents and tools without manual coding. By leveraging MCP, developers can enhance agent development speed, reduce maintenance overhead, and achieve interoperability across Azure and OpenAI platforms.

Amazon Web Services (AWS) now offers OpenAI's open weight models on Amazon Bedrock and SageMaker AI, providing customers with advanced AI capabilities for various applications like agentic workflows, coding, and scientific analysis. This collaboration expands the availability of powerful AI technologies to AWS users, shaping the future of GenAI technology.

The article discusses the author's critical views on AI, particularly focusing on the hype surrounding Generative AI and the author's decision to not fully embrace AI technology. It provides insights into the author's perspective on the current state and future potential of AI, offering a critical viewpoint for AI developers to consider.

Azure AI Foundry is a comprehensive web portal that consolidates various Azure AI services, facilitating collaborative AI project development, model deployment, testing, and integration of custom data sources. AI developers can leverage Azure AI Foundry to create generative AI applications, deploy models to real-time endpoints, define workflows, and incorporate multiple AI capabilities through Azure AI Services.

This article from Tribe AI provides a comprehensive guide to advanced AI governance, focusing on essential strategies for ensuring organizational compliance with advanced AI systems. It covers core principles, challenges, and best practices in AI governance, emphasizing ethics, transparency, and regulatory standards, offering valuable insights for AI developers and leaders in shaping responsible AI adoption.

The article introduces AWS SageMaker Unified Studio, an all-in-one interface that streamlines the machine learning and AI development lifecycle. It highlights the accessibility of data, collaboration features, model training capabilities, and integration with Amazon Q Developer for code assistance, making it suitable for various users from research projects to enterprise teams.

This article discusses the impact of artificial intelligence on jobs from 2025-2030, highlighting the emergence of AI tools like ChatGPT, Google's AI software, Gamma, and Numerous AI. It emphasizes the need for individuals to adapt to the evolving job landscape created by AI, which will both replace and create new job opportunities.

Mosaic AI Agent Bricks simplifies the extraction of structured data from documents like contracts and invoices without manual labeling or schema training. AI developers can learn how to enhance automation and reduce effort by leveraging schema feedback and AI-assisted evaluation for continuous improvement.

This article delves into the common reasons why AI projects fail, highlighting challenges such as unclear success metrics, scope creep, and the added layer of probabilistic uncertainty in AI projects. It provides insights on how organizations can avoid these pitfalls and improve the success rate of their AI initiatives, making it a valuable read for AI developers and builders.

This article reflects on the journey of creative.ai, an early exploration into creative AI, emphasizing the focus on augmenting human creativity rather than replacing it. It discusses the challenges faced, inspirations drawn from past creative computer science works, and the ethos of empowering artists and designers. The open-sourcing of code repositories adds value for AI developers interested in creative AI and historical perspectives on computational creativity.

This article challenges the notion that AI is 'just a tool' and emphasizes the significant impact tools like AI have on society, ethics, and culture. It delves into the complexity of tools beyond their usage, highlighting the importance of considering broader implications in AI development and deployment.

The article introduces 13 foundational AI courses and resources from MIT Open Learning, covering topics like artificial intelligence, machine learning, machine vision, and algorithms. These resources are valuable for AI developers looking to grasp the basics and advance their knowledge in AI technologies.

This article explores the Thought-Action-Observation (TAO) cycle in AI agents, emphasizing how agents reason, act, and learn from their experiences. It delves into the core components of the TAO cycle, such as Thought, Action, and Observation, providing insights and real-world examples for understanding how AI agents operate effectively.

This YouTube video provides a beginner-friendly overview of key AI concepts, including the AI family tree, machine learning, deep learning, generative AI, traditional AI vs. generative AI, large language models, and AI agents. It offers a structured learning path for those new to AI, covering fundamental topics essential for understanding artificial intelligence.

This repository provides a pre-built LangGraph tools agent for the Open Agent Platform, enabling users to work with MCP servers and a LangConnect RAG tool. It includes setup instructions and integration details with the Open Agent Platform, making it a valuable resource for developers interested in agent orchestration and tooling automation.

This repository provides a supervisor agent for the Open Agent Platform, enabling dynamic agent delegation and configurable agents for orchestrating specialist agents. It leverages LangGraph Supervisor and RemoteGraph for agent orchestration.

Azure AI Foundry is a platform for designing and managing AI applications, offering a secure and flexible environment. This article delves into the significance of Azure AI Foundry as an AI app factory, providing insights for AI developers on leveraging this tool for AI application development.

This article delves into Agentic AI architectures and design patterns, focusing on autonomous AI systems that make decisions independently. It discusses key tools like LangChain and AutoGen, design patterns for building complex autonomous systems, and challenges such as coordination in multi-agent setups and potential biases. AI developers can learn about enhancing productivity, real-time data handling, and implementing design patterns for improved AI performance.

Open Agent Platform is an educational platform for building and managing no-code agents with advanced features like RAG integration and agent supervision. It provides a user-friendly interface for creating and interacting with LangGraph agents, making it suitable for users without technical expertise as well as developers.

The video provides a concise overview of Google Agentspace, explaining its functionality and purpose. It offers a quick insight into building AI agents on Google Cloud, making it a valuable resource for AI developers interested in multi-agent systems and cloud-based AI development.

The article delves into the fundamentals and best practices of AI governance, emphasizing the need for a robust framework to manage AI risks and ensure data protection. It provides insights for AI developers and builders on implementing governance strategies to address security threats and compliance requirements.

The article delves into the importance of aligning AI deployments with ethical imperatives, regulatory mandates, and business value. It offers a systematic examination of responsible AI ecosystems, platforms, providers, and regulatory frameworks, providing insights for decision-makers to navigate the complexities of AI governance.

The AI for Beginners curriculum by Microsoft offers a comprehensive 12-week program with 24 lessons covering practical AI concepts, quizzes, and labs. This resource is ideal for AI developers and builders looking to start their journey in artificial intelligence.

This article serves as a comprehensive guide for individuals looking to learn AI from scratch in 2025, offering insights from industry experts, practical advice, and tips on mastering AI skills and tools. It emphasizes the increasing relevance of AI in various industries and provides a roadmap for aspiring data scientists, machine learning engineers, AI researchers, and enthusiasts.

This repository provides a Python library for creating hierarchical multi-agent systems using LangGraph. It allows users to create a supervisor agent to orchestrate multiple specialized agents, with features like tool-based agent handoff mechanism and flexible message history management. The library is built on top of LangGraph, offering support for streaming, memory management, and human-in-the-loop interactions.

This article provides a comprehensive guide to Amazon Bedrock, a managed AWS service for accessing and managing foundation models (FMs) essential for generative AI applications. AI developers can learn how to leverage AWS Bedrock to simplify infrastructure management, access cutting-edge AI models, and develop scalable generative AI applications aligned with their goals.

GraphRAG, a retrieval mechanism enhancing GenAI applications by leveraging graph data structures, is discussed in this article. AI developers can learn how GraphRAG optimizes information retrieval in graph databases, offering insights into improving AI systems' performance.

Open Deep Research is an open-source deep research agent that works across various model providers, search tools, and servers. It has achieved notable performance and rankings on the Deep Research Bench Leaderboard. The repository offers a free course and updates on recent advancements in deep research.

This article provides a walkthrough on building an AI agent observability system using LangChain, Rudderstack, and Clickhouse. It explores the importance of monitoring AI agents in chat systems and delves into the concept of AI observability, offering practical insights for developers interested in enhancing AI system visibility and performance.

This article delves into the transformative impact of effective prompt engineering in AI for solving business challenges. It emphasizes the importance of using specific frameworks to optimize AI models like Claude, Copilot, Perplexity, and others, offering practical insights for AI developers to enhance their prompt creation skills.

Learn how to leverage the COMPLETE function in Snowflake Cortex AI to integrate large language models (LLMs) into SQL queries, enabling advanced AI-driven data interactions. This tutorial by Christopher Marland offers practical examples for generating insights from data and optimizing AI models within Snowflake.

The United Nations System White Paper on AI Governance delves into the institutional models and normative frameworks for global AI governance within the UN system. AI developers can learn about the importance of ethical considerations, data privacy, bias mitigation, and transparent decision-making processes in leveraging AI for positive impacts.

This YouTube video discusses the basics of AI for beginners, focusing on key takeaways from Google's AI Essentials course. The presenter covers topics such as types of AI tools, implied context, zero-shot vs. few-shot prompting, chain-of-thought prompting, and the limitations of AI, providing insights into the pros and cons of the certification course.

This article discusses the process of handling unstructured data using Snowflake Cortex AI for geocoding and geofencing tasks. It provides insights into breaking down complex data problems, utilizing marketplace apps for geocoding services, and scaling up data processing for large datasets.

Caleb Baechtold from Snowflake demonstrates building an application using Cortex Analyst to allow business users to ask data questions in natural language. The walkthrough covers setting up Snowflake, creating a Streamlit app, testing conversational analytics, exploring the semantic model, and more, offering practical insights into leveraging AI for business intelligence.

This article from RAND delves into the root causes of failure for artificial intelligence projects, highlighting insights from data scientists and engineers. It offers recommendations to avoid common pitfalls in AI implementation, making it a valuable resource for AI developers and builders to enhance project success rates.

The article explains GraphRAG, a technique that enhances Retrieval Augmented Generation (RAG) with knowledge graphs, enabling AI models to handle complex tasks like multi-hop reasoning and answering comprehensive questions. AI developers can learn how GraphRAG improves information retrieval and understanding in large language models.

Snowflake Cortex is a fully-managed AI service integrated within Snowflake, enabling businesses to leverage machine learning and AI capabilities through simple SQL commands. It offers pre-built ML functions for tasks like forecasting and anomaly detection, making it easy to gain insights and automate tasks without specialized programming knowledge.

This article provides a framework for the governance of artificial intelligence, emphasizing the importance of values like transparency and truth in AI systems. It offers policymakers strategic methodologies for overseeing AI technologies, promoting trust, accountability, and ethical compliance.

langfuse is an open-source LLM engineering platform that focuses on LLM observability, metrics, evaluations, prompt management, playgrounds, and datasets. It integrates with various technologies like OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. The repository is suitable for individuals interested in exploring large language models and observability in AI systems.

ChainForge is an open-source visual programming environment designed for battle-testing prompts to Large Language Models (LLMs). It enables users to query multiple LLMs simultaneously, compare response quality across different prompts and models, set up evaluation metrics, and streamline the prompt engineering process using AI features.

LangChain is a framework for building context-aware reasoning applications powered by LLM technology. It enables developers to create AI applications by chaining interoperable components and third-party integrations, simplifying AI development and future-proofing decisions as technology evolves.