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Question 237 - Professional Machine Learning Engineer discussion

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You work for a company that is developing an application to help users with meal planning You want to use machine learning to scan a corpus of recipes and extract each ingredient (e g carrot, rice pasta) and each kitchen cookware (e.g. bowl, pot spoon) mentioned Each recipe is saved in an unstructured text file What should you do?

A.
Create a text dataset on Vertex Al for entity extraction Create two entities called ingredient' and cookware' and label at least 200 examples of each entity Train an AutoML entity extraction model to extract occurrences of these entity types Evaluate performance on a holdout dataset.
Answers
A.
Create a text dataset on Vertex Al for entity extraction Create two entities called ingredient' and cookware' and label at least 200 examples of each entity Train an AutoML entity extraction model to extract occurrences of these entity types Evaluate performance on a holdout dataset.
B.
Create a multi-label text classification dataset on Vertex Al Create a test dataset and label each recipe that corresponds to its ingredients and cookware Train a multi-class classification model Evaluate the model's performance on a holdout dataset.
Answers
B.
Create a multi-label text classification dataset on Vertex Al Create a test dataset and label each recipe that corresponds to its ingredients and cookware Train a multi-class classification model Evaluate the model's performance on a holdout dataset.
C.
Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe Evaluate the model's performance on a prelabeled dataset.
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C.
Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe Evaluate the model's performance on a prelabeled dataset.
D.
Create a text dataset on Vertex Al for entity extraction Create as many entities as there are different ingredients and cookware Train an AutoML entity extraction model to extract those entities Evaluate the models performance on a holdout dataset.
Answers
D.
Create a text dataset on Vertex Al for entity extraction Create as many entities as there are different ingredients and cookware Train an AutoML entity extraction model to extract those entities Evaluate the models performance on a holdout dataset.
Suggested answer: A

Explanation:

Entity extraction is a natural language processing (NLP) task that involves identifying and extracting specific types of information from text, such as names, dates, locations, etc. Entity extraction can help you analyze a corpus of recipes and extract each ingredient and cookware mentioned in them. Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides a service for AutoML entity extraction, which allows you to create and train custom entity extraction models without writing any code. You can use Vertex AI to create a text dataset for entity extraction, and label your data with two entities: ''ingredient'' and ''cookware''. You need to label at least 200 examples of each entity type to train an AutoML entity extraction model. You can also use a holdout dataset to evaluate the performance of your model, such as precision, recall, and F1-score. This solution can help you build a machine learning model to scan a corpus of recipes and extract each ingredient and cookware mentioned in them, and use the results to help users with meal planning.Reference:

AutoML Entity Extraction | Vertex AI

Preparing data for AutoML Entity Extraction | Vertex AI

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asked 18/09/2024
Osama Rahman
28 questions
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