[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-mlflow":3},{"tag":4,"articles":11},{"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],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"1f1bff1e-0ebc-4fa7-a078-64dc4b552548","why-databricks-model-serving-is-right-default-en","Why Databricks Model Serving is the right default for production infe…","Databricks Model Serving is the right default for production inference because it unifies deployment, governance, and scaling across model types.","tools","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778692290314-gopj.png","en","2026-05-13T17:10:32.167576+00:00",{"id":22,"slug":23,"title":24,"summary":25,"category":26,"image_url":27,"cover_image":27,"language":19,"created_at":28},"4a3e15ba-07e8-4e4d-b5c8-d9a46deea8bd","aws-s3-sagemaker-unified-studio-fine-tuning-en","AWS uses S3 to speed LLM fine-tuning","AWS shows how SageMaker Unified Studio, S3, and MLflow can fine-tune Llama 3.2 11B Vision Instruct on DocVQA data.","model-release","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775139362238-r31j.png","2026-04-02T14:15:38.340988+00:00"]