Case context: You are leading a machine learning team tasked with building a new system to automatically analyze customer reviews for an e-commerce platform and determine if they are positive or negative. A junior engineer suggests building a pipeline that first uses a parser to identify adjectives and verbs, and then feeds those into a classifier.

Question: Based on recent trends in machine learning design, what alternative architecture should you propose to your team, and how would it process a review like "This is a great mop!" differently from the junior engineer's suggestion?

Sample answer: You should propose an end-to-end sentiment classification system. Instead of the junior engineer's suggested pipeline approach that first passes the text through a parser component, the end-to-end approach uses a single learning algorithm. It would take the raw, original review text "This is a great mop!" directly as input and try to directly predict the sentiment, bypassing the need for intermediate parsing or feature extraction steps.

Key points:

• Propose an end-to-end learning algorithm.
• Recognize that recent trends favor replacing pipeline systems.
• Explain that the system takes the raw, original text as input.
• Note that it directly predicts the sentiment without a parser component.

Rubric: The response must identify the end-to-end approach as the alternative and correctly describe how it bypasses the parser to operate directly on raw text.