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A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input.
Which of the following MLflow operations can be used to perform this task?
Suggested Answer: A
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25 comments · newest first
DE
Desiree
10 months ago
I thought you needed to log the model first to access any schema info.
DO
Dortha
11 months ago
Wait, can you really get schemas from an unlogged model?
LI
Lindsey
11 months ago
I disagree, it has to be C, get_input_schema.
BU
Bulah
11 months ago
Definitely B, mlflow.models.signature.infer_signature!
RI
Ricki
11 months ago
I think it's A, mlflow.models.schema.infer_schema.
DA
Danica
12 months ago
I vaguely recall that C is about getting the input schema directly, but I’m not confident if it applies to unlogged models.
MI
Mira
12 months ago
I practiced a similar question before, and I feel like E could be a trick option since it mentions unlogged models.
OZ
Ozell
12 months ago
I'm not entirely sure, but I remember something about A being used for schema inference. It could be that one?
DO
Dolores
1 year ago
I think the answer might be B, because it sounds like it relates to inferring the model's signature, which includes input and output schemas.
SA
Sabra
1 year ago
I'm pretty confident that the answer is B. The `mlflow.models.signature.infer_signature` function is specifically designed to infer the input and output schemas of a model, regardless of whether it's been logged to MLflow or not. This seems like the most straightforward solution for the given problem.
FR
Frank
1 year ago
I'm a bit confused by this question. I thought we needed to log the model to MLflow first before we could access any information about it. Is there really a way to get the schemas without logging the model? I'll have to think this through carefully.
JE
Jess
1 year ago
Option B sounds like the right choice here. The question is asking about obtaining the input and output schemas, and the `infer_signature` function is designed to do just that, even for unlogged models.
NO
Norah
1 year ago
Hmm, I'm not sure about this one. I know we can use MLflow to log models and get information about them, but I'm not familiar with the specific functions for getting the input and output schemas. I'll have to review the MLflow documentation to see which option is correct.
CH
Cherelle
1 year ago
I think the answer is B. The `mlflow.models.signature.infer_signature` function should be able to infer the input and output schemas of the model, even if it hasn't been logged to MLflow yet.
FR
Francene
1 year ago
Hmm, I'm a bit confused by the options here. I'm not sure which one would be the simplest solution for frequent catalog changes. Maybe I should review the Magento documentation again.
LA
Lawrence
1 year ago
I have a vague recollection of SOAP/XML, but I'm not sure how it fits in with alarms?
FL
Florinda
2 years ago
I believe option B) is the correct one because it specifically mentions inferring the signature, which includes the input and output schema.
KI
Kimi
2 years ago
I'm not sure, but I think option A) mlflow.models.schema.infer_schema might also work.
EL
Elfriede
2 years ago
I agree with Harris. That option makes sense to infer the input and output schema.
HA
Harris
2 years ago
I think option B) mlflow.models.signature.infer_signature can be used.
LO
Lorriane
2 years ago
Tom
ME
Mendy
2 years ago
D) mlflow.models.Model.signature
DO
Dominque
2 years ago
C) mlflow.models.Model.get_input_schema
NI
Nickole
2 years ago
B) mlflow.models.signature.infer_signature
GA
Galen
2 years ago
A) mlflow.models.schema.infer_schema