Design a streaming embedding-based classifier

Q: Design a streaming embedding-based classifier

This question evaluates a candidate's ability to design end-to-end streaming machine learning systems, including online text preprocessing, tokenization, embedding generation, continuous model training, and low-latency classification serving.

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Question

You are given a continuously arriving stream of text data for a classification task. Design an end-to-end machine learning system that:

processes raw text online,
tokenizes the text,
converts tokens into embeddings,
trains a classification model, and
serves low-latency predictions in production.

Explain your choices for data preprocessing, tokenization, embedding generation, model architecture, training strategy, evaluation metrics, and deployment. Also discuss how you would handle large data volume, model updates, and consistency between training and serving.

Design a streaming embedding-based classifier

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