AI Guides
Comprehensive guides that help you navigate AI concepts, best practices, and strategic implementation. From conceptual overviews to detailed methodology explanations.
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Level 400
FeaturedInside Amazon SageMaker Unified Studio: A Unified Data, Analytics ...
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.
Level 300
FeaturedWhy Your Enterprise Needs Google Agent Space | by Biswanath Giri
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.
Level 300
FeaturedInside Google Cloud Agentspace: Overview and UI Walkthrough
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.
Level 300
FeaturedIdentifying and Prioritizing Artificial Intelligence Use Cases ... - Medium
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.
Level 300
FeaturedMy AI Deep Dive and The Use Cases for AI | by Travis Reeder
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.
Level 200
FeaturedTurning ideas into AI use cases - the Product Manager point of view
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.
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Level 400
FeaturedInside Amazon SageMaker Unified Studio: A Unified Data, Analytics ...
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.
Level 300
FeaturedWhy Your Enterprise Needs Google Agent Space | by Biswanath Giri
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.
Level 300
FeaturedInside Google Cloud Agentspace: Overview and UI Walkthrough
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.
Level 300
FeaturedIdentifying and Prioritizing Artificial Intelligence Use Cases ... - Medium
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.
Level 300
FeaturedMy AI Deep Dive and The Use Cases for AI | by Travis Reeder
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.
Level 200
FeaturedTurning ideas into AI use cases - the Product Manager point of view
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.
Level 100
The Future of AI: How Artificial Intelligence Will Change the World
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.
Level 100
MIT report: 95% of generative AI pilots at companies are failing
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.
Level 300
The Complete Google A2A + Azure AI Foundry + Semantic Kernel ...
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.
Level 300
Agent Factory: The new era of agentic AI—common use cases and ...
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.
Level 300
Building Smarter AI Agents with Azure AI Foundry and Model ...
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.
Level 400
OpenAI's open weight models now available on AWS - About Amazon
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.
Level 300
Gentle Intro to Azure AI Foundry. What is it? | by Nicolas Anderson
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.
Level 300
The Ultimate Guide to Advanced AI Governance - Tribe AI
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.
Level 300
Turn Documents into Structured Insights with Mosaic AI Agent Bricks
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.
Level 300
Why AI Projects Fail | Towards Data Science
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.
Level 300
13 foundational AI courses, resources from MIT - Medium
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.
Level 300
V :AI Agents through the Thought-Action-Observation (TAO) Cycle ...
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.
Level 400
Agentic AI Architectures And Design Patterns | by Anil Jain - Medium
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.
Level 200
AI Governance 101: Understanding the Basics and Best Practices
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.
Level 400
The AI Governance Frontier Series Part 1 — Decoding Global and U.S.
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.
Level 200
AI for Beginners
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.
Level 200
How to Learn AI From Scratch in 2025: A Complete Expert Guide
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.
Level 300
Langgraph Supervisor Py
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.
Level 300
Amazon Bedrock: A Complete Guide to Building AI Applications
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.
Level 300
What Is GraphRAG? - Neo4j
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.
Level 300
Build your own AI Agent Observability System | by James Barney
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.
Level 300
The AI prompt solving any business challenge | by Thack - Medium
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.
Level 100
Snowflake Cortex AI | How to Use the COMPLETE Function - YouTube
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.
Level 400
United Nations System White Paper on AI Governance
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.
Level 200
Processing Unstructured Data with Snowflake Cortex AI - Medium
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.
Level 100
The Root Causes of Failure for Artificial Intelligence Projects ... - RAND
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.
Level 400
GraphRAG Explained: Enhancing RAG with Knowledge Graphs
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.
Level 100
What is Snowflake Cortex? - phData
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.
Level 300
Framework for the Governance of Artificial Intelligence - Medium
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.
Level 300
Langchain
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.
Level 200
AI Basics - MIT Sloan Teaching & Learning Technologies
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.
Level 200
It's not all just “AI” - Magnopus
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.
Level 100
How to Learn Artificial Intelligence: A Beginner's Guide - Coursera
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.
Level 400
What Is Agentic AI? | IBM
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.
Level 300
What is Graph RAG | Ontotext Fundamentals
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.
Level 400
What is Agentic RAG? | IBM
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.
Level 400
Welcome - GraphRAG
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.