sagemaker
PulseAugur coverage of sagemaker — every cluster mentioning sagemaker across labs, papers, and developer communities, ranked by signal.
1 天有情绪数据
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FlightSense platform predicts flight delays using AI and propagation features
Researchers have developed FlightSense, an MLOps platform designed to predict flight delays by modeling how delays propagate through aircraft rotation chains. The system achieved an AUC of 0.879 by incorporating delay p…
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Neura Robotics and AWS partner to deploy physical AI in real-world settings
German robotics company Neura Robotics has partnered with AWS to accelerate the deployment of physical AI. The collaboration will see AWS become Neura's primary cloud provider, hosting its Neuraverse platform for traini…
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高效技术团队的机制
Eugene Yan 的文章概述了提高技术团队（尤其是参与机器学习的团队）生产力和有效性的几种机制。关键实践包括用于非正式知识共享和反馈的周终汇报（EOWDs），以及用于深入探讨特定机器学习技术、工具或技能的学习会议。文章还强调了季度回顾的重要性，以确保团队与更广泛的业务和产品优先事项保持一致，并借鉴了 Netflix“高度一致、松散耦合”的理念。
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Data scientists can influence without authority using data and Socratic questioning
Eugene Yan's article offers strategies for data scientists to influence decisions without formal authority, emphasizing the use of data and the Socratic method. He suggests leveraging quantitative and qualitative data t…
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Eugene Yan reflects on Amazon role and prolific writing in 2020
Eugene Yan's 2020 retrospective details his move to Seattle for a new role at Amazon, where he builds recommender and machine learning systems. He emphasizes learning to scale himself through documentation, system desig…
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探讨机器学习研究进展、系统设计模式及战略性问题选择
Eugene Yan 的系列文章探讨了在实际系统中应用机器学习的实用方面。他强调在实施机器学习之前，应先从启发式方法开始项目，设计模式对于高效的数据处理和系统维护的重要性，以及基于成本效益分析仔细选择问题的必要性。Yan 还详细介绍了部署机器学习模型后遇到的常见挑战，如数据污染和反馈循环，并提出了有效的项目管理和系统维护策略。