[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-machine-learning":3},{"tag":4,"articles":11},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"b9520e2e-2ea8-41cb-8948-7fbfa55434fe","machine learning","machine-learning",7,"機器學習涵蓋模型訓練、特徵工程、推論與評估，也延伸到可解釋性、推薦排序、交易策略與代理式 AI。這個標籤聚焦實作與落地案例，幫助讀者看懂模型如何在製造、廣告與金融場景中發揮作用。","Machine learning spans model training, feature engineering, inference, and evaluation, and now reaches explainability, ranking systems, trading bots, and agentic AI. This tag tracks practical use cases and the methods behind them, from manufacturing XAI to ad ranking and production deployment.",[12,21],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"235397ea-a41f-4ff0-aaea-fcad743e2316","microsoft-mlops-maturity-model-five-levels-zh","Microsoft 的 MLOps 五級成熟度模型","Microsoft Azure 把 MLOps 分成五級，從手動訓練到自動監控與重訓。這套模型重點不是打分數，而是看團隊能不能重現、追蹤和自動化模型流程。","industry","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780970578667-kwcy.png","zh","2026-06-09T02:02:30.486328+00:00",{"id":22,"slug":23,"title":24,"summary":25,"category":26,"image_url":27,"cover_image":27,"language":19,"created_at":28},"2ce35a50-85a1-42b5-8d74-af24ccaaf716","llms-knowledge-graphs-ml-explainability-zh","知識圖譜加 LLM 讓製造業 XAI 更好懂","這篇論文把知識圖譜和 LLM 接起來，讓製造業的機器學習結果能被轉成更好懂的解釋。重點不是亂編答案，而是先抓相關圖譜事實，再交給語言模型整理。","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1776665388382-t6m1.png","2026-04-20T06:09:32.525811+00:00"]