January 2023
Intermediate to advanced
288 pages
6h 6m
Chinese
Content preview from 移动端AI与ML应用开发：基于iOS和Android
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第
12
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但是在使用 ML 模型时，当然为了能够有效地提出这些问题，你需要一种方法来部署多
个模型，并根据这些模型对你的受众进行细分。你已经创建了一个模型 v1，并且它运行
良好。你已经从用户那里学到了很多关于它的知识，并收集了新数据来创建新模型。你
希望将其部署给你的一些用户以对其进行测试并进行仔细监控。
你会如何处理这个问题？
作为使用 ML 模型的开发人员，自定义模型托管可以在这里为你提供其他 Firebase 服
务。我们将在本章中探讨一个名为“远程配置”的场景，如果你感兴趣，可以从那里扩
展到平台上可用的其他服务。
因此，首先让我们创建一个有多个模型的场景，为此，我们将回到 TensorFlow Lite
Model Maker。
在使用 Firebase 控制台时，你可能已经注意到不同 API 的许多用法。这些实
际上是针对 ML Kit 的，我们在前面的章节中介绍过！在它自己独立出来之前，
曾经是 Firebase 的一部分，并且它的链接在控制台中仍然可用。
12.2
创建多个模型版本
对于这种情况，你可以使用 TensorFlow Lite Model Maker 创建多个模型的简单测试。你
还可以使用不同的基本规范创建多个模型，而不是使用不同的数据集来查看不同模型的
行为。由于 Model Maker 在内部使用迁移学习，它是创建不同模型的完美工具，理论上
你可以为不同用户部署不同版本，从而查看哪种架构最适合你的场景。
如果回到前面章节中“花”的例子，我们可以获取我们的数据
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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.
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