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Answer: Employ AutoML Natural Language to automatically extract custom entities for categorization, significantly reducing manual preprocessing and development time while allowing for customization.
AutoML Natural Language is the optimal choice because it automates the extraction of custom entities from transcribed calls, minimizing the need for extensive data preprocessing and development time. This approach not only streamlines the classification process but also ensures scalability and cost-effectiveness, enabling quicker routing of incoming calls to the appropriate support team based on the product.
Author: LeetQuiz Editorial Team
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You are a Machine Learning Engineer at a large tech company tasked with modernizing the contact center to improve customer service efficiency. The goal is to classify incoming calls by product to expedite customer requests. The calls have been transcribed using the Speech-to-Text API. Given the constraints of minimizing both data preprocessing and development time, while ensuring the solution is scalable and cost-effective, what is the most efficient approach to construct a model? Choose the best option.
A
Utilize the AI Platform Training with built-in algorithms to develop a customized model, requiring manual feature extraction and model tuning.
B
Develop a custom model from scratch to identify product keywords from the transcribed calls and then apply a categorization algorithm, which involves extensive data preprocessing and development effort.
C
Implement the Cloud Natural Language API to extract custom entities for categorization, which may not fully meet the customization needs for product classification.
D
Employ AutoML Natural Language to automatically extract custom entities for categorization, significantly reducing manual preprocessing and development time while allowing for customization.