May 2024
Intermediate
252 pages
5h 31m
Chinese
Content preview from Ray 分布式机器学习：利用Ray 进行大模型的数据处理、训练、推理和部署
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Ray
概述
|
19
1.3.1 Ray AIR
和数据科学工作流
近年来，数据科学（
D ata Science
，
DS
）发展迅速，连该术语本身都变得有些
难以捉摸，可以在网上找到许多关于数据科学的定义，这些定义都有一定的道
理
注
11
。我们将“数据科学”定义为利用数据获得洞见，并创建实际应用。这是
在实践和应用层面做出的广义定义，重点是理解事物并获得新知。从这个意义
上说，将这个领域的从业者描述为“数据科学家”，就像将黑客描述为“计算机
科学家”一样不准确
注
12
。
12
从宏观上看，数据科学是一个不断迭代的过程，涉及提出需求、收集和处理数
据、搭建和评估模型、部署模型。机器学习不一定属于这个过程，但通常是环
节之一。如果涉及机器学习，可以进一步列出以下步骤：
处理数据
为了训练机器学习模型，需要将数据转换为模型能够理解的格式。将数据经
过转换、选择后再输入模型的过程通常被称为特征工程。特征工程可能非常
烦琐，如果你能熟练使用常用工具进行数据处理，可以节省许多精力。
训练模型
在机器学习中，需要利用处理好的数据训练算法。这个步骤需要选择合适的
算法，如果你能从多种算法中选出恰当的算法，会很有帮助。
调优超参数
在模型训练步骤中，需要对模型进行参数调优。大多数机器学习模型中还有
一组被称为超参数的参数，可以在训练之前对其进行修改。这些参数会对最
终模型的性能产生重大影响，需要进行适当的调优。有一些很好的工具可以
实现自动化超参数调优。
部署模型
训练好的模型需要部署。部署模型意味着用户通过任何途径都可以访问该模
注 11 ：我们向来不喜欢将数据科学归类为数学、编程和商业的交叉学科，因为这样无法指明数据科学家 ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.