Chapter 12. Testing, optimizing, and deploying models
This chapter covers
- The importance of and practical guidelines for testing and monitoring machine-learning code
- How to optimize models trained in TensorFlow.js or converted to TensorFlow.js for faster loading and inference
- How to deploy TensorFlow.js models to various platforms and environments, ranging from browser extensions to mobile apps, and from desktop apps to single-board computers
As we mentioned in chapter 1, machine learning differs from traditional software engineering in that it automates the discovery of rules and heuristics. The previous chapters of the book should have given you a solid understanding of this uniqueness of machine learning. However, machine-learning models and the code surrounding them are still code; they run as a part of your overall software system. In order to make sure that machine-learning models run reliably and efficiently, practitioners need to take similar precautions as they do when managing non-machine-learning code.