Testing and Debugging for Production
You might have gotten excited about training and testing a machine learning model without thinking about the unexpected behavior of your model in production and how your model fits into a bigger technology. Most academic courses don’t go through details of strategies to test models, assess their qualities, and monitor their performance pre-deployment and in production. There are important concepts and techniques in testing and debugging models for production that we will review in this chapter.
In this chapter, we will cover the following topics:
- Infrastructure testing
- Integration testing of machine learning pipelines
- Monitoring and validating live performance
- Model assertion
By the end of this chapter, you will have learned about the importance of infrastructure and integration testing, as well as model monitoring and assertion. You will have also learned how to use Python libraries so that you can benefit from...
