Outline the ML inference and labeling pipeline

Q: Outline the ML inference and labeling pipeline

This is a ML System Design interview question from Roblox for Software Engineer roles. View the full question and solution on PracHub.

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Question

Audio Detection System: ML Inference and Data Pipeline (Model Architecture Out of Scope)

Context and Assumptions

Design the machine learning inference and data pipeline for an audio detection system that flags policy-relevant speech and keywords. Assume:

Near real-time decisions on streaming or short audio chunks, plus offline batch processing for analytics/retraining.
Multi-lingual audio, variable noise conditions, and potential background music.
Model architecture is out of scope; focus on pipeline, features, calibration, thresholds, data contracts, labeling, drift, and versioning.

Requirements

Describe:

Feature extraction choices and ordering:
- Denoising and voice activity detection (VAD).
- Acoustic features (e.g., spectrograms, MFCCs) and embeddings.
- Speech-to-text (ASR) and text features, including keyword spotting.
Inference outputs: how scores are computed, calibrated, and thresholded; how to handle class imbalance.
Data contracts for model inputs/outputs and storage plan for transcripts, embeddings, and intermediate artifacts.
Label generation: manual labeling workflows and how to feed labels back (active learning).
Drift detection and operational versioning of models and thresholds.

Outline the ML inference and labeling pipeline

Audio Detection System: ML Inference and Data Pipeline (Model Architecture Out of Scope)

Context and Assumptions

Requirements

Solution (Locked)

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