Evaluating Your ML Project with the MLOps Checklist

This document is a guide that presents the MLOps checklist as a tool for evaluating machine learning (ML) projects. It outlines the components of the checklist, which can be utilized at any phase of an ML project to assess overall readiness and identify areas for improvement. The checklist consists of nine components, including data-centric management, experimentation, observability and model management, robust pipelines and promotion, continuous integration, continuous monitoring, continuous deployment, continuous training, and governance. Each component is described in detail, emphasizing the importance of data management, experimentation tracking, and the integration of various processes in a mature MLOps system. The guide also recommends the use of the checklist at the start of an MLOps project while noting that parts of it can be applied during any phase. The document serves as a comprehensive resource for ML practitioners aiming to enhance their MLOps practices.