13 ML development hubris
This chapter covers
- Applying refactoring to overengineered implementations to increase development velocity
- Identifying code to target for refactoring
- Establishing simplicity-driven development practices
- Adopting new technologies via sustainable means
- Comparing build, buy, and prior art in implementations
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 ...
Get Machine Learning Engineering in Action now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
