How do you test your machine learning and AI solutions before deployment?

1 Define your test objectives

Before you start testing your machine learning and AI solutions, you need to define your test objectives clearly and explicitly. What are the expected outcomes and metrics of your solutions? What are the acceptable levels of accuracy, precision, recall, and other relevant measures? What are the potential risks and errors that you want to avoid or mitigate? How will you validate and verify your solutions against your test objectives? By defining your test objectives, you will be able to design and execute your tests more efficiently and effectively.

2 Use test data sets

One of the most important aspects of testing your machine learning and AI solutions is to use test data sets that are representative, relevant, and realistic. Test data sets are data sets that are separate from the training and validation data sets that you use to build and tune your models. Test data sets should reflect the characteristics and distribution of the real-world data that your solutions will encounter in production. Test data sets should also cover various scenarios, edge cases, and anomalies that may affect your solutions. By using test data sets, you will be able to evaluate how well your solutions generalize and perform on new and unseen data.

3 Apply test automation

Another best practice for testing your machine learning and AI solutions is to apply test automation whenever possible. Test automation is the process of using software tools and frameworks to automate the execution and evaluation of your tests. Test automation can help you save time, resources, and effort, as well as reduce human errors and biases. Test automation can also enable continuous testing, which is the practice of testing your solutions frequently and regularly throughout the development and deployment lifecycle. Some of the common tools and frameworks for test automation include pytest, unittest, Selenium, PyTorch Lightning, TensorFlow Extended, and MLflow.

4 Monitor and update your solutions

Finally, testing your machine learning and AI solutions does not end with deployment. You need to monitor and update your solutions continuously to ensure that they are still functioning properly and meeting your test objectives. Monitoring your solutions involves collecting and analyzing data on their performance, behavior, and feedback from users and stakeholders. Updating your solutions involves retraining, fine-tuning, or replacing your models and algorithms based on the results of your monitoring. By monitoring and updating your solutions, you will be able to maintain their quality, reliability, and performance over time.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?