The preceding chapter focused on critical components used to measure a project’s overall health from a purely prediction-focused and solution efficacy perspective. ML projects that are built to support longevity through effective and detailed monitoring of their inputs and outputs are certainly guaranteed to have a far higher success rate than those that do not. However, this is only part of the story.
Another major factor in successful projects has to do with the human side of the work. Specifically, we need to consider the humans involved in supporting, diagnosing issues with, improving, and maintaining the project’s code base over the lifespan of the solution.