  An increasingly complex and diverse collection of Machine Learning (ML)
models as well as hardware/software stacks, collectively referred to as "ML
artifacts", are being proposed - leading to a diverse landscape of ML. These ML
innovations proposed have outpaced researchers' ability to analyze, study and
adapt them. This is exacerbated by the complicated and sometimes
non-reproducible procedures for ML evaluation. A common practice of sharing ML
artifacts is through repositories where artifact authors post ad-hoc code and
some documentation, but often fail to reveal critical information for others to
reproduce their results. This results in users' inability to compare with
artifact authors' claims or adapt the model to his/her own use. This paper
discusses common challenges and pitfalls of ML evaluation and benchmarking,
which can be used as a guideline for ML model authors when sharing ML
artifacts, and for system developers when benchmarking or designing ML systems.
