Скачать с ютуб AI Engineering Insights from Chip Huyen’s Book | Chapter 4: Evaluating AI Systems в хорошем качестве

AI Engineering Insights from Chip Huyen’s Book | Chapter 4: Evaluating AI Systems

𝗔𝗜 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗮𝘀 𝗔𝗜 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁—𝗵𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗮𝘀𝘀𝗲𝘀𝘀 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆? In this video, we dive into 𝘾𝙝𝙖𝙥𝙩𝙚𝙧 4 𝙤𝙛 𝘾𝙝𝙞𝙥 𝙃𝙪𝙮𝙚𝙣’𝙨 𝙗𝙤𝙤𝙠, 𝘼𝙄 𝙀𝙣𝙜𝙞𝙣𝙚𝙚𝙧𝙞𝙣𝙜: 𝘽𝙪𝙞𝙡𝙙𝙞𝙣𝙜 𝘼𝙥𝙥𝙡𝙞𝙘𝙖𝙩𝙞𝙤𝙣𝙨 𝙬𝙞𝙩𝙝 𝙁𝙤𝙪𝙣𝙙𝙖𝙩𝙞𝙤𝙣 𝙈𝙤𝙙𝙚𝙡𝙨 𝙖𝙣𝙙 break down the 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. 📌 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂’𝗹𝗹 𝗟𝗲𝗮𝗿𝗻 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗩𝗶𝗱𝗲𝗼: ✅ Why 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗔𝗜 is more than just accuracy ✅ Challenges of 𝗼𝗽𝗲𝗻-𝗲𝗻𝗱𝗲𝗱 𝗔𝗜 𝗼𝘂𝘁𝗽𝘂𝘁𝘀 & 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀 ✅ AI evaluation criteria: 𝗗𝗼𝗺𝗮𝗶𝗻-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆, 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻-𝗙𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴, 𝗖𝗼𝘀𝘁 & 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 ✅ Detecting 𝗔𝗜 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀—AI as a judge, self-verification & knowledge-augmented verification ✅ 𝗛𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 for real-world AI applications ✅ Real-world applications in 𝗳𝗶𝗻𝗮𝗻𝗰𝗲, 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲, 𝗮𝗻𝗱 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 ✅ AI evaluation tools: 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲, 𝗢𝗽𝗲𝗻𝗔𝗜 𝗘𝘃𝗮𝗹𝘀, 𝗠𝗟𝗙𝗹𝗼𝘄, 𝗣𝗶𝗻𝗲𝗰𝗼𝗻𝗲 & 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲 💡 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗽𝗲𝗿𝗳𝗲𝗰𝘁—𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗶𝘁 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆 𝗲𝗻𝘀𝘂𝗿𝗲𝘀 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆. Learn how to assess AI models before deployment! 📢 𝗗𝗶𝘀𝗰𝗹𝗮𝗶𝗺𝗲𝗿: This video is based on my 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗶𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝘁𝗶𝗼𝗻 of 𝘈𝘐 𝘌𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨: 𝘉𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘈𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴 𝘸𝘪𝘵𝘩 𝘍𝘰𝘶𝘯𝘥𝘢𝘵𝘪𝘰𝘯 𝘔𝘰𝘥𝘦𝘭𝘴 𝘣𝘺 𝘊𝘩𝘪𝘱 𝘏𝘶𝘺𝘦𝘯. It is not an official summary, and all views expressed are my own. 🔔 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 & 𝘀𝘁𝗮𝘆 𝘁𝘂𝗻𝗲𝗱 𝗳𝗼𝗿 𝗖𝗵𝗮𝗽𝘁𝗲𝗿 𝟱, 𝘄𝗵𝗲𝗿𝗲 𝘄𝗲 𝗱𝗶𝘀𝗰𝘂𝘀𝘀 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻! #AIEngineering #MachineLearning #ArtificialIntelligence #FoundationModels #AIEvaluation #SoftwareArchitecture #ChipHuyen #AIModelEvaluation