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Databricks Exam Databricks-Machine-Learning-Professional Topic 2 Question 24 Discussion

Actual exam question for Databricks's Databricks-Machine-Learning-Professional exam
Question #: 24
Topic #: 2
[All Databricks-Machine-Learning-Professional Questions]

A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.

Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

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Suggested Answer: D

by Chantell at Sep 10, 2024, 07:05 PM

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Emmett
1 months ago
Wow, this engineer really likes to get their hands dirty, huh? Wrapping the preprocessing in a custom model class? That's some next-level stuff. But hey, if it works, it works. I'm just glad I don't have to worry about all that technical mumbo-jumbo. I'll just stick to the multiple-choice questions and leave the coding to the experts!
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Cecil
5 days ago
B) The same preprocessing logic will automatically be applied when calling fit
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Lasandra
18 days ago
A) The pvfunc model can be used to deploy models in a parallelizable fashion
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Odette
2 months ago
Well, this is an interesting approach. I can see the benefit of having the preprocessing baked into the model itself. It'll definitely make deployment a lot simpler. Although, I wonder if it might be a bit of a hassle to maintain that custom model class down the line. Ah, the joys of machine learning engineering!
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Lorita
2 days ago
User 1: I agree, having the preprocessing built into the model does simplify deployment.
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Winfred
14 days ago
Francine: True, but I can see how it might become a bit of a headache to maintain that custom model class
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Francine
1 months ago
User 2: Definitely, it's a great way to ensure consistency in the preprocessing step
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Alline
1 months ago
User 1: I agree, having the preprocessing built into the model does simplify things
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Anastacia
2 months ago
Haha, this sounds like the engineer wanted to reinvent the wheel. Why not just use a good old pipeline? That's what they're designed for, to handle all the preprocessing and modeling in one neat package. But I guess everyone has their own style, right?
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Nathan
7 days ago
True, everyone has their own approach to solving problems.
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Santos
1 months ago
Maybe the engineer wanted more control over the preprocessing steps.
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Dorothy
1 months ago
I agree, using a pipeline would have been simpler and more efficient.
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Margart
2 months ago
Hmm, I'm not sure if this approach is really necessary. Doesn't MLflow already handle the preprocessing for you when you log a model? Oh well, I guess the engineer wanted to be extra thorough. As long as it works, I'm happy!
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Marjory
2 months ago
Ah, I see! So the preprocessing is baked right into the model itself. That's clever, saves me the hassle of managing a separate preprocessing step. Now I can just load up the pyfunc model and let it handle everything. Neat!
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Eugene
19 days ago
Antione: And it's great for downstream deployment too. Everything is taken care of within the model itself.
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Eladia
20 days ago
User 3: It definitely streamlines the process. Just load the pyfunc model and you're good to go.
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Antione
1 months ago
User 2: That's so convenient. No need to worry about separate preprocessing steps anymore.
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Malcom
2 months ago
User 1: Yes, exactly! The preprocessing is automatically applied when calling fit and predict.
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Virgie
2 months ago
I believe option C is the correct answer then. It makes the deployment process smoother.
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Tamekia
2 months ago
I agree with Kanisha. It's convenient to have the preprocessing automatically applied during prediction.
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Kanisha
2 months ago
I think the benefit of this approach is that the same preprocessing logic will automatically be applied when calling predict.
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