Deep learning (DL) is a subset of machine learning
that uses multi-layer neural networks to learn representations from
data such as images, audio, and text. It can reduce some manual
feature engineering, but useful results still depend on
representative data, appropriate baselines, careful evaluation, and
controls for the intended environment.
Our work focuses on the path from evaluation to responsible
use.
Engagements can include problem framing, non-AI baselines, data and
error analysis, model optimization, system integration, monitoring,
and deployment planning. For edge and mobile uses, model size,
latency, energy use, privacy, and failure behavior are evaluated
against the constraints of the target device and workflow.
