Kubeflow
PulseAugur coverage of Kubeflow — every cluster mentioning Kubeflow across labs, papers, and developer communities, ranked by signal.
1 天有情绪数据
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MLOps成为AI部署超越模型训练的关键
MLOps正日益成为在生产环境中部署和维护机器学习模型的关键学科。虽然模型训练曾是主要焦点，但MLOps的运营方面现在被认为对现实世界的AI应用更为重要。这包括部署、服务和管理模型的策略，并特别关注与传统ML模型相比，大型语言模型（LLMs）所面临的独特挑战。各种工具和架构，例如使用Docker、Flask、AWS和MLflow的工具和架构，对于构建健壮的MLOps管道至关重要。
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Kubeflow pipeline automates model training, validation, and deployment
This article details the process of building a complete MLOps pipeline using Kubeflow. It focuses on automating the entire workflow, from training a machine learning model to registering it, validating its performance, …
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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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Ubuntu 19.10 boosts AI/ML development and edge Kubernetes
Canonical has released Ubuntu 19.10, focusing on enhancing AI/ML development and edge computing capabilities. The update includes improved support for Kubernetes at the edge via MicroK8s and integrates Kubeflow for mach…