AI Guides

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.

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 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 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.

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.