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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213
第
12
章
使用 Firebase 产品化应用程序
到目前为止，在本书中，你已经探索了使用机器学习来创建模型，并且你研究了如何使
用各种技术将它们集成到 Android 或 iOS 应用程序中。你可以使用 TensorFlow Lite 进
行底层操作，直接使用模型，并处理与模型之间的数据转换过程。或者对于许多常见场
景，你可以利用 ML Kit 使用具有异步编程方法的高级 API 来更轻松地构建响应式应用
程序。但是在所有这些情况下，你只是构建了一个非常简单的应用程序，它在单个活动
或视图中进行推理。
在将应用程序产品化时，你当然必须走得更远，而 Firebase 是一种跨平台解决方案，旨
在帮助你构建、发展应用并从应用中获利。
虽然对 Firebase 的全面讨论超出了本书的范围，但 Firebase 中有一个重要功能可在免费
（又 名 Spark）层中使用，你可以真正利用它：自定义模型托管。
12.1
为什么要使用
Firebase
自定义模型托管
正如你在本书中所看到的，创建一个 ML 模型来为你的用户解决问题并不困难。它相对
简单，这要归功于 TensorFlow 或 TensorFlow Lite Model Maker 等工具，它们可以根据
你的数据快速训练模型。很难做的是创建正确的模型，基于明显的假设，要能够做到这
一点，你需要不断地与用户一起测试和更新你的模型，验证它的性能，而不仅是从速度
或准确性的角度思考，要考虑它如何影响人们对你的应用程序的使用。正确的模型会带
来更好的表现吗？错误的模型是否意味着用户会退出你的应用 ...
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