精选理由
用SageMaker跑基准和推荐任务，结果自动流到MLflow，省去手动导出的麻烦，做实验跟踪更省心了。
Amazon SageMaker AI发布了与MLflow的新集成，支持将优化推理推荐任务和基准任务的指标、参数和图表实时流式传输到无服务器MLflow App。用户无需手动导出数据，即可在统一界面跟踪实验。此集成简化了模型评估和部署流程，提升了A/B测试和模型比较的效率。
原文 · AWS Machine Learning Blog
Streaming benchmark and recommendation results to MLflow with Amazon SageMaker AI
In this post, you learn how to use the new MLflow integration with Amazon SageMaker AI optimized inference recommendation jobs and Amazon SageMaker AI benchmark jobs to automatically stream experiment data into a unified tracking interface. This integration streams metrics, parameters, and charts into your serverless Amazon SageMaker MLflow App in real time and you get a unified experiment tracking experience.