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Google Professional Machine Learning Engineer Exam - Topic 6 Question 66 Discussion

You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories You have a labeled dataset in Cloud Storage You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency What should you do?
D) Train the model by using AutoML, and expose the model as a Vertex Al endpoint Configure your mobile application to invoke the endpoint during prediction.
A) Train the model by using AutoML, and register the model in Vertex Al Model Registry Configure your mobile application to send batch requests during prediction.
B) Train the model by using AutoML Edge and export it as a Core ML model Configure your mobile application to use the mlmodel file directly.
C) Train the model by using AutoML Edge and export the model as a TFLite model Configure your mobile application to use the tflite file directly

Google Professional Machine Learning Engineer Exam - Topic 6 Question 66 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 66
Topic #: 6
[All Professional Machine Learning Engineer Questions]

You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories You have a labeled dataset in Cloud Storage You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency What should you do?

Show Suggested Answer Hide Answer
Suggested Answer: D

Applying quantization to your SavedModel by reducing the floating point precision can help reduce the serving latency by decreasing the amount of memory and computation required to make a prediction. TensorFlow provides tools such as the tf.quantization module that can be used to quantize models and reduce their precision, which can significantly reduce serving latency without a significant decrease in model performance.


by Lai at Oct 22, 2023, 12:31 PM

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Gladis
9 months ago
Batch requests? That's not efficient for mobile!
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Amie
10 months ago
Wait, can AutoML Edge really handle that?
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Brittni
10 months ago
Not sure about that, TFLite might be faster.
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Gladys
10 months ago
I agree, Core ML is perfect for mobile apps!
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Ayesha
10 months ago
Option B seems like the best choice for iOS.
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Gianna
10 months ago
I feel like using Vertex AI for model registration is a good idea, but I’m not sure if batch requests are the best approach for a mobile app.
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Ona
11 months ago
I’m a bit confused about whether to use batch requests or direct model invocation. I feel like direct invocation could be faster, but I need to double-check.
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Gail
11 months ago
I remember practicing a similar question where we had to minimize latency. I think exporting as a TFLite model might help with that.
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Cecily
11 months ago
I think using AutoML Edge could be the right choice since it’s designed for mobile applications, but I'm not entirely sure about the export formats.
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Alfred
11 months ago
This is a good opportunity to showcase my understanding of Google Cloud's AI/ML services. I'm confident I can analyze the question and select the most appropriate solution.
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Yasuko
11 months ago
Okay, I see the main tradeoffs are between using AutoML, AutoML Edge, and Vertex AI. I'll need to think through the pros and cons of each in terms of cost, latency, and model deployment for the mobile app.
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Janella
11 months ago
Hmm, I'm a bit unsure about the differences between the deployment options presented. I'll need to carefully review the details of each approach to determine the most cost-effective and low-latency solution.
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Miesha
11 months ago
This looks like a pretty straightforward question about deploying a no-code image classification model for a mobile app. I think I've got a good handle on the key considerations here.
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Merilyn
11 months ago
This looks like a straightforward question about the actions Server Protect can take when it detects an infected file. I'll need to carefully read through the options and select the three correct actions.
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Veronika
11 months ago
This question seems straightforward, but I want to make sure I understand the key tasks that text processing doesn't support well for requirements management.
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Carry
11 months ago
I think the answer is A. SAS Information Delivery Portal sounds like the right tool to surface different types of business and analytic content on the web.
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Tatum
11 months ago
This looks like a straightforward question on programming operators used in business process rules. I'll go through the options carefully and select the three that are supported.
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Gwen
1 year ago
Yo, I heard AutoML is like the easy mode of machine learning. Might as well just go with that and let the experts handle the hard stuff, am I right?
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Latanya
1 year ago
D) AutoML definitely simplifies the process. I think option A sounds like the way to go.
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Nobuko
1 year ago
C) Train the model by using AutoML Edge and export the model as a TFLite model Configure your mobile application to use the tflite file directly
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Lisha
1 year ago
B) Yeah, AutoML does make things easier. I'd go with that option for sure.
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Fredric
1 year ago
A) Train the model by using AutoML, and register the model in Vertex AI Model Registry Configure your mobile application to send batch requests during prediction.
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Coral
1 year ago
I'm gonna go with D. Vertex AI endpoint seems like the easiest option, even if it might cost a bit more.
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Lou
1 year ago
Yeah, it might be a bit more expensive, but it's worth it for the ease of use.
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Tran
1 year ago
That sounds like a good choice. It's convenient to use the Vertex AI endpoint for predictions.
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Cristy
1 year ago
D) Train the model by using AutoML, and expose the model as a Vertex AI endpoint Configure your mobile application to invoke the endpoint during prediction.
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Dusti
1 year ago
Hmm, I'm not sure. Option A with the batch requests might be a bit slower, but at least I don't have to worry about the model deployment.
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Alisha
1 year ago
I agree, Option C with the TFLite model could also be a good choice for low latency.
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Alisha
1 year ago
I think Option B might be better for minimizing latency, using the mlmodel file directly.
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Jani
1 year ago
Option A sounds good, batch requests can help with prediction speed.
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Gregoria
1 year ago
Option C looks good to me. Exporting the TFLite model and using it directly in the mobile app should give us the lowest possible latency.
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Cherilyn
1 year ago
I think option B is the way to go. Training with AutoML Edge and using the Core ML model directly on the mobile app sounds like the best approach to minimize cost and latency.
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Levi
1 year ago
Yeah, using AutoML Edge and exporting as a Core ML model for direct use on the mobile app makes sense.
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Layla
1 year ago
I agree, option B seems like the most efficient choice for this scenario.
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Kimberely
1 year ago
That's a valid point, but I still think option A provides better scalability and flexibility for future updates in the model.
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Bulah
1 year ago
I disagree, I believe option B is more suitable as it utilizes AutoML Edge and Core ML model for direct integration with the mobile application.
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Kimberely
1 year ago
I think option A is the best choice because it involves using AutoML and Vertex AI Model Registry for efficient model training and prediction.
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