15 Quality and acceptance testing

This chapter covers

  • Establishing consistency for data sources used in ML
  • Handling prediction failures gracefully with fallback logic
  • Providing quality assurance for ML predictions
  • Implementing explainable solutions

In the preceding chapter, we focused on broad and foundational technical topics for successful ML project work. Following from those foundations, a critical infrastructure of monitoring and validation needs to be built to ensure the continued health and relevance of any project. This chapter focuses on these ancillary processes and infrastructure tooling that enable not only more efficient development, but easier maintenance of the project once it is in production.

Between the completion of model ...

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.