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Optimize AI: Build Fast Efficient Pipelines

本课程是 Deep Learning Engineering 专项课程的一部分

位教师：ansrsource instructors

包含在中

了解更多

1个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 小时完成

灵活的计划

自行安排学习进度

1个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 小时完成

灵活的计划

自行安排学习进度

您将获得的技能

您将学习的工具

AI Workflows

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Deep Learning Engineering 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有1个模块

In this short, hands-on course, you’ll learn how to build fast, efficient AI training and inference pipelines by optimizing both data loading and computational graphs. You’ll start by creating parallel, high-throughput data pipelines that keep GPUs consistently busy and reduce training bottlenecks. Then you’ll analyze a model’s computational graph to identify and remove redundant operations that slow execution. Through focused lesson videos, practical labs, and guided coach activities, you’ll re-export a streamlined model and validate real latency improvements. By the end, you’ll be able to diagnose performance issues, streamline pipelines, and apply optimization techniques that make AI systems faster, more reliable, and more cost-efficient.