How are machine learning models tested? And where can testing go wrong?


 Min Read

There is no denying that effectively testing a machine learning (ML) model is extremely difficult. Since machine learning is fairly young and constantly evolving, testing processes keep changing. Necessary experimentation with models adds layers upon layers of complexity, causing rapid transformations and increasing the risk of potentially disastrous errors—like data bias or model drift. So, what can be done? In this blog we will:

  1. Explore ML model testing best practices.
  2. Outline the challenges teams encounter when testing their models.
  3. Explain how model monitoring and machine learning operations (MLOps) technology are catalysts for effective ML testing. 

How do you test the accuracy of machine learning models?

Testing a model is no easy feat. Developers have to juggle dynamic input/output relationships, black box models, and unexpected model degradation. To combat these significant hurdles, the following machine learning model testing techniques have been established:

Testing robustness 

Testing the robustness of a model helps developers determine if their model will remain stable even as data and input/output relationships change in real-time. 

Testing interpretability

Testing the interpretability of an ML model allows developers to understand if the algorithm correctly interprets existing and new datasets. This tests the model’s ability to predict outputs, identify underlying biases, and highlights how input variables contribute to the model’s output.

Testing reproducibility

Being able to replicate results is a crucial component of creating an effective ML model. Reproducibility means developers can run their algorithm repeatedly on new datasets and maintain consistent results. 

When testing the robustness, interpretability, and reproducibility of a model, the following testing methodologies should be implemented:

Machine learning unit testing

Unit testing works on a small scale, checking the accuracy of small pieces of code, or units, in a model. This helps teams determine if each piece of code is performing as intended. 

Machine learning regression testing

Regression testing determines if a model will malfunction and helps identify previous bugs. This form of testing is critical since developers need to identify recurring bugs as models are regularly being retrained.

Machine learning integration testing 

Integration testing determines if all aspects of a model work well together within the ML pipeline. This means testing if a model functions correctly end-to-end. 

These testing techniques lay out a solid framework, but significant challenges remain. Many of these tests come with their own difficulties and are hard to implement themselves. In the next two sections, we’ll take a look at some of these challenges, and discuss how teams can successfully scale testing processes. 

What are the challenges of testing a machine learning model?

Although teams face unique challenges depending on the specific ML model they’re developing, there are common challenges that all teams face during the training and testing phases of the MLOps lifecycle

AI bias

Without consistent monitoring throughout all phases of ML model development, model bias is allowed to creep in. When biased data flies under the radar, it can cause real damage very quickly. For example, Amazon’s AI recruitment tool was discovered to be biased against women.

The “black box” problem

The complex and obscure nature of ML models makes it extremely difficult to obtain in-depth visibility, often resulting in sub-par solutions and potentially harmful outcomes. This is especially concerning when ML models are being deployed in healthcare settings, when life-or-death decisions are being made. Explainable AI is essential to unlocking the black-box and creating transparent AI systems.

Data drift

This lack of visibility also leads to data drift. When undetected, which it often is, data drift breaks functionality and corrupts the model performance

So, what can you do? How can teams gain greater visibility and control over the ML testing process? The answer: AI Observability.

How to test the accuracy of a machine learning model responsibly with AI observability

Since machine learning models are completely different from traditional software systems, teams struggle with maintaining high-quality models in production. AI observability allows you to determine how and when issues are occurring in real-time. This added transparency not only makes it easier for your team to identify issues, it also affords you greater insight into how to solve these issues and prevent them from reoccurring. Here are just a few ways an AI observability can help you navigate the challenges outlined above:

  • Validates models prior to launch
  • Continuously monitors model performance in production
  • Proactively addresses model bias
  • Explains past predictions

At Fiddler, we’re here to help you build trust into AI. Our platform empowers you to take control, notifying you as soon as issues arise so you can problem-solve before deployment. Our platform also integrates with your existing machine learning testing tools and processes so you can easily adapt your workflows. Try Fiddler for free today.