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连通系统与机器学习的 MLOps 挑战在哪？这篇文章讲清楚了
What is the MLOps challenge of connecting systems and machine learning? This article makes it clear

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贾琪钰内蒙古农业大学

时间：2026-06-08 语向：中-英类型：人工智能字数：229

连通系统与机器学习的 MLOps 挑战在哪？这篇文章讲清楚了
What Are the Core MLOps Challenges When Integrating Systems with Machine Learning? This article breaks them down thoroughly
离线部署相对更加直接，它涉及到单一的、不定期运行的大批量预测工作。常见的设置包含商业智能、保险评估和医疗健康分析；
Offline deployment is comparatively straightforward. It covers one-off, ad-hoc batch prediction workloads. Typical use cases include business intelligence, insurance underwriting and healthcare analytics.
离线部署相对更加直接，它涉及到单一的、不定期运行的大批量预测工作。常见的设置包含商业智能、保险评估和医疗健康分析；
Offline deployment is comparatively straightforward. It covers one-off, ad-hoc batch prediction workloads. Typical use cases include business intelligence, insurance underwriting and healthcare analytics.
在线部署属于网络应用，如果用户想要自己的查询得到快速响应，则需要快速、低延迟的预测。
Online deployment targets web-facing applications, where predictions must deliver high throughput and low latency to return instant results for user queries.
在线部署属于网络应用，如果用户想要自己的查询得到快速响应，则需要快速、低延迟的预测。
Online deployment targets web-facing applications, where predictions must deliver high throughput and low latency to return instant results for user queries.

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连通系统与机器学习的 MLOps 挑战在哪？这篇文章讲清楚了 What is the MLOps challenge of connecting systems and machine learning? This article makes it clear

连通系统与机器学习的 MLOps 挑战在哪？这篇文章讲清楚了
What is the MLOps challenge of connecting systems and machine learning? This article makes it clear