Google Professional Machine Learning Engineer
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You are an ML engineer at a social media company tasked with developing a visual filter to enhance users' profile photos across various platforms. Specifically, you need to train a machine learning model that can detect and create bounding boxes around human faces. This feature is intended for your company's iOS-based mobile phone application. To ensure the model performs efficiently on iOS devices, you aim to minimize code development efforts and optimize the model for mobile inference. What approach should you take?
You are an ML engineer at a social media company tasked with developing a visual filter to enhance users' profile photos across various platforms. Specifically, you need to train a machine learning model that can detect and create bounding boxes around human faces. This feature is intended for your company's iOS-based mobile phone application. To ensure the model performs efficiently on iOS devices, you aim to minimize code development efforts and optimize the model for mobile inference. What approach should you take?
Explanation:
The correct answer is A: Train a model using AutoML Vision and use the 'export for Core ML' option. AutoML Vision simplifies the process of training machine learning models by automating many aspects of the process, thus minimizing code development and expertise required. Exporting the model for Core ML makes it compatible with iOS devices, ensuring the model is optimized for mobile inference. Core ML is Apple's machine learning framework designed specifically for iOS and macOS, providing efficient and high-performance inference on Apple devices.