Exam Professional Machine Learning Engineer topic 1 question 92 discussion

https://cloud.google.com/vision/automl/docs/export-edge Core ML -> iOS and macOS Coral -> Edge TPU-based device TensorFlow.js -> web

Went with A

https://developer.apple.com/documentation/coreml Answer A

AutoML Vision is a service provided by Google Cloud that enables developers to train and deploy machine learning models for image recognition tasks, such as detecting bounding boxes around human faces. The “export for Coral” option generates a TFLite model that is optimized for running on Coral, a hardware platform specifically designed for edge computing, including mobile devices. The TFLite model is also compatible with iOS-based mobile phone applications, making it easy to integrate into the company's app.

Option A, using AutoML Vision and exporting for Core ML, is also a viable option. Core ML is Apple's machine learning framework that is optimized for iOS-based devices. However, using this option would require more development effort to integrate the Core ML model into the app. Option C, using AutoML Vision and exporting for TensorFlow.js, is not the best option for this scenario since it is optimized for running on web browsers, not mobile devices. Option D, training a custom TensorFlow model and converting it to TFLite, would require significant development effort and time compared to using AutoML Vision. AutoML Vision provides a simple and efficient way to train and deploy machine learning models without requiring expertise in machine learning.

https://www.tensorflow.org/lite https://medium.com/the-ai-team/step-into-on-device-inference-with-tensorflow-lite-a47242ba9130

I correct myself: it's A!

A indeed as described here: https://cloud.google.com/vision/automl/docs/export-edge

A "You want to minimize code development" -> AutoML - https://cloud.google.com/vision/automl/docs/tflite-coreml-ios-tutorial - https://cloud.google.com/vertex-ai/docs/training-overview#image

TensorFlow Lite is a lightweight version of TensorFlow that is optimized for mobile and embedded devices, making it an ideal choice for use in an iOS-based mobile phone application.

I find no answer is 100% right, but D seems closer to the truth.