Start by clarifying requirements (latency SLA, throughput, data volume, privacy constraints) and then walk through the pipeline stage by stage, justifying each design choice with trade-offs. Emphasize Apple's on-device and privacy-first context by discussing hybrid edge-cloud deployment and efficient model architectures.
Pro tip: Quantify trade-offs with concrete numbers (e.g., 'batching 32 samples adds 50ms latency but triples throughput') and mention Apple-specific frameworks like Core ML and ML Compute to show domain awareness.
Ask about latency SLA, throughput targets, data modality, privacy requirements, and deployment environment (edge vs cloud). This ensures your design aligns with Apple's priorities like on-device processing and user privacy.
Outline the stages: ingestion (e.g., Kafka, gRPC streams), online tokenization (e.g., SentencePiece, BPE with caching), embedding generation (e.g., transformer encoder), and classification head. Discuss how to handle out-of-order events and backpressure.
Explain dynamic batching (e.g., time-based or size-based) to balance latency and throughput. Discuss techniques like micro-batching, asynchronous processing, and model quantization to meet SLAs.
Choose architectures (e.g., distilled transformers, CNNs for text) based on latency and accuracy. Describe training: pre-train embeddings, fine-tune classifier, use techniques like knowledge distillation and quantization-aware training for deployment.
Define metrics (latency, throughput, accuracy, drift) and logging. Discuss A/B testing, shadow deployment, and retraining triggers. Highlight privacy-preserving monitoring (e.g., on-device analytics).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.