Google Professional Machine Learning Engineer
Get started today
Ultimate access to all questions.
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.
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.
Explanation:
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.