12 Holding on to your gains by watching for drift
This chapter covers
- Identifying and monitoring for drift in production solutions
- Defining responses to detected drift
In the preceding chapter, we established the foundations for measuring the effectiveness of an ML solution. This solid base enables a DS team to communicate to the business about the performance of a project in terms that are relevant to the business. To continue making (hopefully) positive reports about the effectiveness of a solution, a bit more work needs to be done.
If proper attribution monitoring and reporting to the business are the bedrock and foundation of a project, entropy is the buffeting storm seeking to continuously tear down the project. We call this chaotic shift ...
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.
