Chapter 9. Beyond Features
This chapter looks at the next steps for implementing practical AI features in your Swift apps using tools that go beyond the ones that Apple supplies, or that work directly with Apple’s tools. We go beyond the feature-focused tasks, and look at tasks that can improve your workflow or help you out in other ways.
We also look at useful extensions to the Apple ML and AI ecosystem, and give you some pointers for what to explore next. Taking a top-down approach, we explore tasks that go beyond implementing AI features in your apps using CoreML and Apple’s frameworks.
Specifically, here are the six tasks explored in this chapter:
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Installing Swift for TensorFlow:: Setting up and running with the latest version of Swift for TensorFlow.
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Using Python with Swift:: A look at using the popular, essential, ubiquitous Python with Swift (via Swift for TensorFlow).
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Training a classifier using Swift for TensorFlow:: Building an image classifier using Swift for TensorFlow.
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Using the CoreML Community Tools:: Using Apple’s Python framework, CoreML Community Tools, to manipulate and convert models from other formats.
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On-device model updates:: Using on-device personalization to make changes to CoreML models on a device.
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Downloading models on device:: Downloading a CoreML model from a server and compiling it on a device.
Task: Installing Swift for TensorFlow
TIP: For a reminder on what Swift for TensorFlow is, check back to Chapter 2, specifically “Tools from ...
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