Are you using machine learning models to improve the experience on your applications? You must have realized that it is a very complex process to verify the proper functioning and implementation of these ML models. When you are testing AI models and features, it is very important to set a proper workflow and strategic approach.
Not accustomed to testing AI models? Don’t worry! We are here for you. Our article will help you understand why you need a separate process for ML test cases compared to your traditional approach. We’ll also help you understand some of the most important frameworks and best practices to easily perform this process.
Why ML Model Testing
If you compare machine learning models with traditional testing processes, you will see that they can learn patterns from data compared to following hardcoded instructions. This is because the behavior of these models is mostly probabilistic and data-dependent. This is yet another reason why the validation process becomes even more complex.
To shed more light on this segment, let us divert our attention towards some of the major reasons that justify why machine learning model testing is very important:
- It plays a very important role in ensuring the reliability of the model even when it is exposed to various working conditions.
- The ML testing workflow will also help you verify that the model can perform efficiently and accurately when it is exposed to various unforeseen data.
- It will play a very important role in ensuring the fairness of the overall testing model. This is because it can help you detect and remove biases in the prediction process.
- You should also consider the safety aspect of this entire implementation process. This will become even more useful in high-tech fields like healthcare or autonomous driving
technologies.
- Finally, if you are verifying the functioning of your machine learning models, you are taking a step forward towards ensuring data and privacy compliances. This is because it will help you abide by all the legal and regulatory standards, like the AI Act and GDPR.
Challenges In Testing ML Models
While testing the ML models, there will be certain challenges that you will encounter during this process. To ensure that you are not facing any unwanted obstacles during the test implementation process, we have mentioned all the major challenges of this step below:
- The machine learning and artificial intelligence models often show a non-deterministic behavior. This happens when it produces different results even with the same data. The most common reason behind this scenario is when the testers fail to control the randomness behind the test implementation process.
- The data dependency of ML models is yet another obstacle that you need to fix during the test implementation process. This is because the performance of these models are highly sensitive to the quality and distribution of the training and testing data that you will be using during the execution processes.
- While you are implementing ML models over multiple changing testing environments, the performance and stability of these models can massively vary. Moreover, the change of the production environments can differ depending on the training environments, which can, in turn, introduce distribution shifts in the data.
- You should always remember that accuracy isn’t the only factor that will drive the dependency of the AI models. Precision, recall, F1 score, ROC AUC, and others are also equally important, depending on the use case of your AI-based testing model.
- Finally, you should also find all the hidden biases that might be present within the infrastructure of the ML model. To perform this process, you must include fairness and bias direction mechanisms, especially while working on crucial sectors like recruitment or lending.
Types of ML Model Testing
To further help you understand the influence of machine learning model testing, let us divert our attention towards some of the most important areas of the application testing process that can benefit from the integration of machine learning:
Unit Testing For ML Model Testing
Before verifying the proper functioning of the entire application architecture, it is equally important to test the individual components that will be present on the application. To perform this process, you can use unit testing, where unit refers to all the individual elements on your app.
Some of the most important elements that you can test with this process include Feature Transformers or data loaders. AI-based testing frameworks like PyTest and Unittest will be highly useful to properly implement this process.
Integration Testing
Now that you have verified the proper functioning of all the individual elements present in an application, it is time to verify the functioning of the application after all these elements have been integrated together. To perform this process, you have to rely on integration testing.
With integration testing, you are aiming to verify the proper interactions between data preprocessing, model training, and Inference pipelines.
Validation Testing
Using validation testing, you can verify how well a model performs on a validation dataset. The major goal of this process is to verify the proper functioning of the entire ML model when you start feeding all the real-world data sets to it for the execution process.
To properly verify the execution of validation testing, you can use certain metrics like RMSE, MAE, accuracy, and a confusion matrix.
Performance Testing
With performance testing in the machine learning models, you are aiming to benchmark training and inference speed under various conditions that you will expose the ML model to. If you are aiming to improve the efficiency of performance test cases, you can rely on some of the popular tools like TensorBoard and MLPerf.
Robustness Testing
While you are using AI models to run the test cases, it will be a common scenario when the model has to process various unreliable datasets to ensure the stability of the entire system.
To verify that your machine learning model is actually capable of performing this process, you can implement robustness testing where you will be applying noise or other adversarial samples to ensure the resistance of the testing model.
Fairness And Bias Testing
In most cases, you will see that machine learning models and artificial intelligence workflows often develop a bias depending on the training data. Due to these biases, it often results in unreliable test results about the quality of the application that you are testing.
You can measure the bias using statistical parity, disparate impact, and with tools like Fairlearn and IBM AI Fairness 360.
Post-Deployment Testing
After you have deployed the AI model for the testing process, you can include post-deployment testing with monitoring for data, thrift, concept drift, and prediction stability.
Best Practices for ML Model Validation
Finally, we would suggest you to incorporate the following practices while you are implementing ML model verification processes within your application testing workflow. While creating these practices, we ensured that we are covering all the possible use cases and Improvement aspects that you can implement in this step:
- We would suggest you to establish a validation strategy at the earlier phases of the development cycle. To implement this process, you can add machine learning testing and validation in the ML development life cycle right from the start.
- It is a very wise decision to automate the repetitive test cases. This will not only save a lot of testing time but will also improve the overall accuracy of the testing cycle. To perform this step, you can add continuous integration and continuous deployment tools like GitHub Actions or Jenkins, which will, in turn, automate the testing during the model updating process.
- It is equally important to perform cross-platform validation of the machine learning workflow. To use this step, you can rely on tools like Bootstrapping and K Fold, which can reduce overfitting and ensure stability of the entire architecture.
- We strongly recommend you to track the model versioning right from the ideation and training process all the way to the test execution step. You can use MLflow or DVC to track the model versions and all the related artifacts. This step would be equally important to ensure the stability and intercompatibility of all the included architectures and elements.
- While you are using AI tools for developers, it is important to start investing in AI real device testing platforms like LambdaTest, which can help you run real device test cases through remote servers. To explain more, LambdaTest is an AI-native test orchestration and execution platform that lets you perform manual and automation testing at scale with over 3000+ browsers, OS combinations, and 5000+ real devices.
- While you are verifying the stability and proper functioning of your ML models, you should also test them for bias and fairness. During this process, it is a good idea to include demographic or sensitive attributes to further strengthen the accuracy of these validation steps.
- We would strongly advise you to implement continuous monitoring for data and concept drift. If you want to achieve this step, you can easily use tools like Evidently or Wild Labs, which will continuously monitor the functioning of the ML model-based test cases.
- Finally, it will be a great Idea to involve domain experts in the test execution and validation steps. You can include humans in the loop, especially in healthcare, finance, or legal domains to justify all the decisions and actions taken by the machine learning and artificial intelligence models. This will be an extra step towards maintaining the proper authority and integrity of your system.
Apart from these best practices, you should also understand that there will be certain strategies which need to be customized depending on your own requirements. To properly perform this process, you should have a clear idea about your requirements and the target for the application that you are working on.
The Bottom Line
Based on all the areas that we discussed in this article, you can easily come to the conclusion that testing ML models is much more important than just going to the surface level and checking the accuracy of the workflow. You should also implement all the strategies and best practices that we have given in this article.
It is also equally important to start understanding that performing test cases is not the final checkpoint, but instead it is an ongoing process to ensure that the artificial intelligence model serves humanity reliably, safely, and fairly.
Apart from these practices, you should also constantly update yourself regarding all the upcoming trends and innovations in the segment of artificial intelligence and testing AI components. This will be a very important approach to not only upgrade your skills, but also create a positive reputation for the brand you’re working with.
