[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-mlflow":3},{"tag":4,"articles":11,"peer_article_count":66},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"f562db8d-7e57-4a83-837e-602867b2f0d7","MLflow","mlflow",3,"MLflow 是用來管理機器學習實驗、模型版本與部署流程的開源平台，常和 MLOps、SageMaker、S3 一起出現。它讓訓練參數、指標、模型產物與追蹤紀錄可重現，也方便比較不同資料量與微調設定的效果。","MLflow is an open-source platform for tracking experiments, versioning models, and moving them through training and deployment workflows. It matters in MLOps because it keeps parameters, metrics, artifacts, and run history reproducible across fine-tuning setups, cloud pipelines, and model comparisons.",[12,21,29,37,44,51,58],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"91637501-ee43-4951-b43c-ce2ba3299d3a","mlops-roadmap-2026-turns-learning-into-delivery-zh","MLOps 路線圖把學習變交付","我把一份 MLOps 路線圖拆成可照做的交付順序，從基礎到上線、監控與模板都能直接抄。","tools","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782567219485-a9wf.png","zh","2026-06-27T13:33:06.891797+00:00",{"id":22,"slug":23,"title":24,"summary":25,"category":26,"image_url":27,"cover_image":27,"language":19,"created_at":28},"ee90f738-3cd0-4dc4-bd65-922e7290c910","genie-code-databricks-ml-command-center-zh","Genie Code 把 Databricks 變 ML 指揮台","我拆 Databricks 的 Genie Code 更新，整理成可直接套用的 ML 工作流模板、提示詞與審核節點。","ai-agent","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782004690739-5z02.png","2026-06-21T01:17:42.754321+00:00",{"id":30,"slug":31,"title":32,"summary":33,"category":34,"image_url":35,"cover_image":35,"language":19,"created_at":36},"1ca3cf77-7688-45c3-ad99-ecf7c0ec7f54","mlops-zoomcamp-path-to-production-ml-zh","MLOps Zoomcamp 把模型帶上線的完整路線","9 個免費模組、14.8k 星標，從實驗追蹤到部署監控與最終專案，幫你判斷這門 MLOps 課程是否適合把模型推進 production。","industry","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781542984202-6g6y.png","2026-06-15T17:02:28.556043+00:00",{"id":38,"slug":39,"title":40,"summary":41,"category":17,"image_url":42,"cover_image":42,"language":19,"created_at":43},"47ce5058-3c10-4d7c-ad89-053b8f8d953e","databricks-custom-models-aws-overview-zh","Databricks AWS 自訂模型重點","Databricks 說明如何在 AWS 上打包、部署與擴展自訂模型，重點是 MLflow、CPU\u002FGPU 選擇、冷啟動、重載規則與容量規劃。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780378383303-0zt9.png","2026-06-02T05:32:34.88582+00:00",{"id":45,"slug":46,"title":47,"summary":48,"category":17,"image_url":49,"cover_image":49,"language":19,"created_at":50},"663ba1ef-830f-4002-b418-a594068f30fa","smarthire-mlflow-initial-access-zh","SmartHire 把 MLflow 變初始存取","我拆 SmartHire 的 MLflow 繞過、Pickle RCE 和可寫 plugin 提權，最後整理成可直接抄的攻擊路徑。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779419808086-rps3.png","2026-05-22T03:16:04.182767+00:00",{"id":52,"slug":53,"title":54,"summary":55,"category":17,"image_url":56,"cover_image":56,"language":19,"created_at":57},"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":59,"slug":60,"title":61,"summary":62,"category":63,"image_url":64,"cover_image":64,"language":19,"created_at":65},"39da7a6f-9cdb-4df7-b624-4a6e65102f6f","aws-s3-sagemaker-unified-studio-fine-tuning-zh","AWS 用 S3 加速 LLM 微調","AWS 示範怎麼用 SageMaker Unified Studio、S3 和 MLflow，拿 DocVQA 資料微調 Llama 3.2 11B Vision Instruct，並比較 1,000、5,000、10,000 筆資料的訓練效果。","model-release","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775139361040-b83t.png","2026-04-02T14:15:37.601407+00:00",10]