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Google Professional Machine Learning Engineer Exam - Topic 6 Question 82 Discussion

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?
D) Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).
A) Train a model using AutoML Vision and use the ''export for Core ML'' option.
B) Train a model using AutoML Vision and use the ''export for Coral'' option.
C) Train a model using AutoML Vision and use the ''export for TensorFlow.js'' option.

Google Professional Machine Learning Engineer Exam - 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

by Della at Jul 11, 2024, 12:48 AM

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Elvis
7 months ago
Totally agree with A, it's straightforward!
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Farrah
8 months ago
Wait, can TensorFlow Lite really optimize that well?
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Van
8 months ago
AutoML Vision is super efficient for this!
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Cheryll
8 months ago
I think D might be better for custom needs.
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Deonna
8 months ago
A is the best choice for iOS apps!
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Cordelia
9 months ago
I’m leaning towards training a custom TensorFlow model and converting it to TFLite, but I’m not confident about the export options.
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Van
9 months ago
I feel like I saw something about exporting for Coral in a study session, but I don't think that's relevant for iOS apps.
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Joesph
9 months ago
I remember practicing a question about model optimization for mobile, and I think TensorFlow Lite could be a good choice for that.
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Cortney
9 months ago
I think using AutoML Vision and exporting for Core ML makes sense since it's for iOS, but I'm not completely sure if that's the best option.
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Angelo
9 months ago
I like the idea of minimizing code development, so I'm inclined to go with option A. Exporting the AutoML Vision model for Core ML sounds like the most straightforward approach.
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Gerri
9 months ago
Option B, exporting for Coral, is interesting, but I don't think that's the best fit since we're targeting an iOS app, not an edge device. I'm leaning towards A or D.
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Kelvin
9 months ago
Hmm, I'm a bit unsure about this one. I'm wondering if option D, training a custom TensorFlow model and converting it to TFLite, might give me more control and flexibility. I'll have to think about the tradeoffs.
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Allene
9 months ago
I think I'll go with option A. Using AutoML Vision and exporting for Core ML seems like the easiest way to get a model optimized for iOS deployment.
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Erin
9 months ago
This looks like a tricky networking question. I'm not too familiar with the specifics of ONTAP cluster networking, so I'll need to think this through carefully.
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Fletcher
9 months ago
Alright, I've got a strategy for this. I'll go through each statement and try to recall which Oracle module or application typically owns each of the shared entities listed.
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Carli
10 months ago
Therapeutic substitution means different chemical entity, right? So we're talking about swapping drugs within the same class. I'm betting approval matters here.
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Glory
1 year 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
1 year 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
1 year ago
Exactly! And we can ensure that the model is specifically tailored for inference on mobile phones.
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Chantell
1 year ago
Definitely! Plus, having that custom TensorFlow model will give us more control over the optimization process.
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Nan
1 year 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
1 year ago
D) Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).
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Stanton
1 year ago
C) Train a model using AutoML Vision and use the ''export for TensorFlow.js'' option.
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Colette
1 year ago
B) Train a model using AutoML Vision and use the ''export for Coral'' option.
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Louann
1 year ago
A) Train a model using AutoML Vision and use the ''export for Core ML'' option.
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Genevive
1 year 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
1 year 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 year ago
Frederick: Absolutely, optimizing for mobile is crucial. Custom models can give us more control over the process.
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Frederick
1 year 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
1 year 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
1 year 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 year 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 year ago
Let's go with option A then. It's the most efficient choice for our project.
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Brunilda
1 year ago
I agree, Core ML is optimized for iOS, so it should work seamlessly with our mobile application.
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Yuriko
1 year ago
Option A sounds like the way to go. AutoML Vision can handle the heavy lifting for us.
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Noemi
1 year 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
1 year ago
I agree with Fredric, using Core ML would optimize the model for iOS devices.
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Fredric
1 year ago
I think we should train a model using AutoML Vision and export for Core ML.
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