Model & Pipeline Review
Find out what your training stack is actually doing.
Most ML systems accumulate quiet problems: an input pipeline that starves the GPU, a validation split that leaks, an evaluation metric that flatters, a serving path that nobody has load-tested. A review surfaces them before they surface themselves.
What we examine
- Architecture and modeling choices — is the model the right size and shape for the data volume and the deployment target, and is there a defensible baseline it beats?
- Data pipeline — tf.data structure, throughput against accelerator demand, caching and shuffling correctness, preprocessing placement.
- Training stability — learning-rate schedules, normalization behavior, mixed-precision numerics, seed discipline and run-to-run variance.
- Evaluation methodology — split hygiene and leakage, metric choice against the operating point, test-set discipline.
- Serving path — export format, conversion parity, latency on the real target, monitoring and rollback.
What you receive
A written findings report, ranked by expected impact, with a measured improvement target attached to each finding — throughput, accuracy at the operating point, latency, or cost — and the reasoning behind it. Findings come with concrete fixes, and where a fix is small we will often have verified it during the review itself.
How it runs
Reviews are hourly engagements, typically a small number of weeks with one or two senior engineers reading code, profiling runs, and interviewing the team. We work in your repositories and your infrastructure; nothing leaves your environment.