Adaptation and Inference Review

Compare frozen, retrieval, and parameter-efficient approaches; then quantize the candidate and measure quality, latency, memory, and cost.

Evidence and review

Defend the selected system and fallback. Claims must trace to versioned code and artifacts. Screenshots, unexplained generated prose, and a single favorable run do not pass.

Oral defense

Explain the mechanism, identify the most dangerous assumption, interpret one failure from raw evidence, and name the condition that would reverse your decision.

Source backbone

Use Dive into Deep Learning, PyTorch documentation, primary papers, and Designing Data-Intensive Applications selectively.