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Google Exam Professional Machine Learning Engineer Topic 6 Question 82 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 82
Topic #: 6
[All Professional Machine Learning Engineer Questions]

You work as an ML engineer at a social media company, and you are developing a visual filter for users' profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company's iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones. What should you do?

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Suggested Answer: D

Contribute your Thoughts:

Glory
2 months ago
Wait, is this a trick question? I'm gonna play it safe and go with option A. Ain't nobody got time for custom TensorFlow models when you can just let the AI do the heavy lifting. Now, where's the nearest coffee shop?
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Crissy
2 months ago
Oooh, decisions, decisions. I'm gonna have to go with option D. Sure, it might take a bit more work, but think of the bragging rights! I'll be the envy of all the other ML engineers at the company picnic.
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Kanisha
20 days ago
Exactly! And we can ensure that the model is specifically tailored for inference on mobile phones.
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Chantell
21 days ago
Definitely! Plus, having that custom TensorFlow model will give us more control over the optimization process.
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Nan
24 days ago
I think option D is the way to go. It might be more work, but the results will be worth it!
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Albina
29 days ago
D) Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).
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Stanton
1 months ago
C) Train a model using AutoML Vision and use the ''export for TensorFlow.js'' option.
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Colette
1 months ago
B) Train a model using AutoML Vision and use the ''export for Coral'' option.
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Louann
2 months ago
A) Train a model using AutoML Vision and use the ''export for Core ML'' option.
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Genevive
2 months ago
Alright, time to put on my tech-savvy hat. Option A seems like the most efficient choice here. I mean, who wants to get bogged down in the nitty-gritty of model training when you can just let AutoML do its thing?
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Yong
3 months ago
Hmm, I'm feeling like a rebel today. Option D sounds like the way to go - train a custom TensorFlow model and then convert it to TensorFlow Lite. Gotta keep those mobile users happy, am I right?
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Nohemi
1 months ago
Frederick: Absolutely, optimizing for mobile is crucial. Custom models can give us more control over the process.
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Frederick
1 months ago
User 2: Yeah, that's a solid approach. Keeping the mobile users in mind is key for a smooth experience.
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Caprice
2 months ago
User 1: Option D sounds like a good choice. Custom TensorFlow model converted to TensorFlow Lite for mobile optimization.
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Deangelo
3 months ago
Whoa, looks like we've got a real brainteaser here! I'd go with option A - keep it simple and let AutoML Vision do the heavy lifting. Plus, Core ML is optimized for iOS, so it's a no-brainer.
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Galen
1 months ago
Great, we'll train the model using AutoML Vision and export it for Core ML. That should work well for our iOS app.
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Kaycee
1 months ago
Let's go with option A then. It's the most efficient choice for our project.
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Brunilda
1 months ago
I agree, Core ML is optimized for iOS, so it should work seamlessly with our mobile application.
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Yuriko
3 months ago
Option A sounds like the way to go. AutoML Vision can handle the heavy lifting for us.
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Noemi
3 months ago
I prefer training a custom TensorFlow model and converting it to TensorFlow Lite for more control over the model.
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Lacey
3 months ago
I agree with Fredric, using Core ML would optimize the model for iOS devices.
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Fredric
4 months ago
I think we should train a model using AutoML Vision and export for Core ML.
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