[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-mlops":3},{"tag":4,"articles":11},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"791a2399-2c92-4bb9-9616-7583f83ba2d7","MLOps","mlops",6,"MLOps 指的是把模型訓練、驗證、部署與監控串成可重複的工程流程，讓 ML 團隊能穩定交付模型。它涵蓋 CI\u002FCD、特徵管理、模型版本控管、推理服務與漂移監測，也和 Kubernetes、GPU 基礎設施密切相關。","MLOps is the engineering layer that makes model training, validation, deployment, and monitoring repeatable. It covers CI\u002FCD, feature and model versioning, inference serving, drift detection, and the infrastructure choices that tie ML systems to Kubernetes and GPUs.",[12,21,28,35,42,50,57,64,71,78,85,93,100,107],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"4923364e-f9c3-42fc-ae92-89ee5a822575","how-to-hire-mlops-engineer-2026-zh","怎麼招到 MLOps 工程師","這篇教你在 2026 年招到適合的 MLOps 工程師，從定義職責、設定薪資、找人、面試到快速錄用。","industry",null,"zh","2026-06-04T19:17:26.372485+00:00",{"id":22,"slug":23,"title":24,"summary":25,"category":17,"image_url":26,"cover_image":26,"language":19,"created_at":27},"fea00cb7-b390-45c7-8555-7d15365fa186","mlops-production-breaks-2026-zh","2026 年 MLOps 為何還會壞掉","MLOps 到 2026 年已是 AI 上線後的標配，但模型、資料和成本一變，生產環境還是會壞。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780215480701-n76w.png","2026-05-31T08:17:29.427912+00:00",{"id":29,"slug":30,"title":31,"summary":32,"category":17,"image_url":33,"cover_image":33,"language":19,"created_at":34},"b099f2da-c719-4b0c-8ce4-25e6195907e2","5-mlops-goals-for-production-teams-zh","5 個 MLOps 目標，讓生產團隊更好上線","5 個 MLOps 目標一次看懂：從部署、可重現性到監控與治理，幫生產團隊判斷先做哪一項最有用。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780214577864-zafh.png","2026-05-31T08:02:27.430551+00:00",{"id":36,"slug":37,"title":38,"summary":39,"category":17,"image_url":40,"cover_image":40,"language":19,"created_at":41},"ec3b4adc-049e-4ed9-8637-9902fb961f8d","5-reasons-mlops-community-2-0-matters-zh","5 個 MLOps Community 2.0 關鍵原因","5 個原因看懂 MLOps Community 轉入 Agentic AI Foundation 後，對實務社群、活動與開放討論的影響。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779793561039-ry9d.png","2026-05-26T11:05:23.523857+00:00",{"id":43,"slug":44,"title":45,"summary":46,"category":47,"image_url":48,"cover_image":48,"language":19,"created_at":49},"7e4fb371-259b-40c1-a0da-52936db22028","automlops-four-investments-agentic-ml-zh","AutoMLOps：4 項投資重點","Jam with AI 在 2026 年 5 月 21 日提出 AutoMLOps：把代理式實驗接到 MLOps 上，但前提是指標、評估器與管線都夠成熟。","tools","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779416153446-7pou.png","2026-05-22T02:15:28.014197+00:00",{"id":51,"slug":52,"title":53,"summary":54,"category":47,"image_url":55,"cover_image":55,"language":19,"created_at":56},"e7117b0e-1bd5-4a92-a511-5f2d5720d922","mlops-cost-myths-gpu-waste-zh","MLOps 成本迷思讓 GPU 不再亂燒","拆掉「多買 GPU 就會更快」的迷思，給你一份可直接抄進團隊文件的 MLOps 成本控制模板。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779415599255-xpdz.png","2026-05-22T02:05:58.283925+00:00",{"id":58,"slug":59,"title":60,"summary":61,"category":17,"image_url":62,"cover_image":62,"language":19,"created_at":63},"bf65e913-62a5-4ac6-bfdb-5d4dd7ab3527","mlops-in-2026-architecture-strategy-guide-zh","2026 年 MLOps 架構與策略指南","2026 年的 MLOps 重點在治理、LLMOps 整合與成本控制。企業已把 AI 放進 production，但多數還卡在試點到擴張的落差。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778610668465-auam.png","2026-05-12T18:30:46.602039+00:00",{"id":65,"slug":66,"title":67,"summary":68,"category":17,"image_url":69,"cover_image":69,"language":19,"created_at":70},"c68e2e73-f14d-4b34-9353-bfa18ec613f4","why-mlops-matters-more-than-devops-for-ai-systems-zh","為什麼 MLOps 比 DevOps 更重要：AI 系統的可靠性關鍵","MLOps 不是 DevOps 的附屬品，而是 AI 系統在生產環境中保持可重現、可監控、可回滾的必要紀律。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778610055401-m2wy.png","2026-05-12T18:20:24.542465+00:00",{"id":72,"slug":73,"title":74,"summary":75,"category":17,"image_url":76,"cover_image":76,"language":19,"created_at":77},"f57d2afa-7a99-40c3-870a-06290956b5db","why-2026-ai-engineer-roadmap-wrong-starting-point-zh","為什麼 2026 AI 工程師路線圖不是最佳起點","2026 AI 工程師路線圖太寬，適合當參考，不適合當第一份學習計畫。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777860652350-3s7g.png","2026-05-04T02:10:24.315161+00:00",{"id":79,"slug":80,"title":81,"summary":82,"category":47,"image_url":83,"cover_image":83,"language":19,"created_at":84},"feb9176d-89c6-4bd0-a82a-8440625d8c94","awesome-open-source-ai-projects-list-zh","開源 AI 專案清單怎麼挑","這份 GitHub 清單收錄可直接上線的開源 AI 專案，從 PyTorch 到 vLLM 都有，2,486 顆星，適合想找模型、推理、RAG 和代理工具的工程師。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775999036470-b4zr.png","2026-04-12T13:03:35.795784+00:00",{"id":86,"slug":87,"title":88,"summary":89,"category":90,"image_url":91,"cover_image":91,"language":19,"created_at":92},"ae3d05a6-e02d-4126-a009-439b60a574ee","matei-zaharia-2025-acm-prize-ai-infrastructure-zh","Matei Zaharia 拿下 2025 ACM Pri…","Matei Zaharia 以 Spark 和 Ray 拿下 2025 ACM Prize。這兩套系統撐起大型資料處理、機器學習與 AI 基礎設施，也反映 AI 競爭已轉向系統層。","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775823358164-tqif.png","2026-04-10T12:15:40.075062+00:00",{"id":94,"slug":95,"title":96,"summary":97,"category":17,"image_url":98,"cover_image":98,"language":19,"created_at":99},"b2f9469b-f74a-44b1-9e08-8b1539632542","kubernetes-becoming-ais-control-plane-zh","Kubernetes 正在變成 AI 控制平面","KubeCon Europe 2026 釋出明確訊號：Kubernetes 正從容器編排，轉向 AI 基礎設施控制平面，重點落在 inference、GPU 與開放標準。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775178595353-m3ll.png","2026-04-03T01:09:30.415473+00:00",{"id":101,"slug":102,"title":103,"summary":104,"category":47,"image_url":105,"cover_image":105,"language":19,"created_at":106},"8ebda40b-9172-4a86-bc27-52f4d301f210","mlops-explained-how-ml-teams-ship-models-zh","MLOps 是什麼？ML 團隊怎麼上線模型","MLOps 把模型訓練、測試、部署和監控變成可重複流程。這篇用 AWS 的視角，拆解它怎麼運作、為何重要，以及和 DevOps 的差別。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775143133896-37yf.png","2026-04-02T15:18:31.788287+00:00",{"id":108,"slug":109,"title":110,"summary":111,"category":47,"image_url":112,"cover_image":112,"language":19,"created_at":113},"e6f5e2d9-233e-4103-83e2-2c9e09883ec7","2026-ai-roadmap-repo-ml-agentic-ai-zh","2026 AI 路線圖：從 ML 到 Agent","一個只有 1 顆星的 GitHub repo，卻把 2026 年從 ML 基礎、GenAI 到 agentic AI 的學習路線排得很完整。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775199980318-nz4a.png","2026-04-01T09:33:31.750053+00:00"]