Integration testing of machine learning pipelines
When we train a machine learning model, we need to evaluate how well it interacts with the other components of a larger system it belongs to. Integration testing helps us in validating that the model works correctly within the overall application or infrastructure and meets the desired performance criteria. Some of the important components of integration testing to rely on in our machine learning projects are as follows:
- Testing data pipelines: We need to evaluate that the data preprocessing components before model training, such as data wrangling, are consistent between the training and deployment stages.
- Testing APIs: If our machine learning model is exposed through an API, we can test the API endpoints to ensure it handles requests and responses correctly.
- Testing model deployment: We can use integration testing to assess the model’s deployment process, whether it’s deployed as a standalone service,...
