Why should ML Engineers care about testing AI models before deployment?
by
Daniel Angelov
,
Feb 15, 2023
There are multiple ways to ensure an ML model is robust - but we need to be able to measure it.
As the demand for AI grows, the role of ML Engineers and Researchers becomes increasingly more complex, with more responsibilities and tasks added to their plates. In this fast-paced and dynamic field, it can be tempting to prioritise other tasks over extensive testing and rely on superficial dataset evaluations. However, testing AI models before deployment is of utmost importance and should not be overlooked. Despite the growing demands of the MLOps space, the consequences for the businesses and communities of deploying an untested or inaccurate model can be significant and long-lasting. It is imperative for ML Engineers to understand the crucial role that testing plays in the development and deployment of machine learning models.
Here are just some of the reasons why ML testing should be brought up in the priority list:
Testing helps ensure model accuracy and reliability: By thoroughly testing a machine learning model, ML Engineers can identify any potential issues or inaccuracies before deployment, allowing them to improve the model and increase confidence in its performance.
Avoids potential legal and ethical issues: Machine learning models have the potential to make decisions that can have real-world consequences, such as discrimination or bias. Testing can help identify these issues before deployment and mitigate the risk of harm.
Improves user trust and satisfaction: Customers and users of AI models expect the technology to work reliably and accurately. Thorough testing before deployment can help build trust in the technology and increase user satisfaction.
Helps identify performance bottlenecks: Testing can uncover performance issues that may negatively impact the model's ability to scale and perform well in a production environment.
Improves development and maintenance process: Extensive testing helps ML Engineers identify and track changes to the model over time, making it easier to maintain and improve the model in the future.
Better collaboration and knowledge sharing: ML Engineers can share test results and learn from each other, improving collaboration and encouraging knowledge sharing within the team and organization.
In conclusion, testing AI models before deployment is a crucial step in the development and deployment process, helping to ensure accuracy, reliability, and avoiding potential legal and ethical issues. By thoroughly testing machine learning models, ML Engineers can improve the quality of their work and increase confidence in the technology they are deploying.
The output of the model starts changing when slight perturbations are added - e.g. we would like to detect boats on sunny and overcast days.
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