Skill UI

Provides comprehensive guidelines for achieving high reproducibility in Machine Learning Systems (MLSys) performance claims. It details how to rigorously control sources of non-determinism (e.g., thermal noise, random seeds) and pin the entire technical stack—from hardware and drivers to frameworks and serving policies—to ensure results can be validated by third parties.

A comprehensive guide to modeling the specific peer review cycle of MLSys submissions. It details the OpenReview workflow, the challenges posed by the mixed ML and systems reviewer pool, and provides strategic advice on addressing concerns related to workload representativeness, baseline strength, and evidence scope matching for high-stakes technical papers.

A comprehensive project management guide for the MLSys conference submission cycle. It covers the entire research lifecycle, from initial venue selection and track choice through paper submission, review responses, artifact evaluation, and final conference presentation. The guide emphasizes backward planning, managing critical resources like shared GPU time, and mitigating common failure modes.