In order to streamline robustness testing into the AI engineering process, the SecureAI team has made it a priority to integrate robustness testing tools into AI Singapore’s MLOps pipelines. In this article, we will share our experience integrating Microsoft CheckList, as a black-box robustness testing tool, in our CI/CD processes along with various technologies as
Standard model evaluation processes involve measuring the accuracy (or other relevant metrics) on a hold-out test set. However, the performance on these test sets do not always reflect the ability of the model to perform in the real world. This is because a fundamental assumption when deploying AI models is that all future data is
With more AI systems being deployed into production, it becomes critical to ensure that the systems are secure and trustworthy. Here in AI Singapore, the SecureAI team is dedicated to developing processes and tools to support the creation of secure and trustworthy AI systems. As shared in the previous article, one of the key ingredients
Ensuring robustness in AI systems against malicious attacks garners greater attention Over the past few years, Artificial Intelligence (AI) has exploded in capability and is becoming increasingly common in our everyday lives. From Tesla’s Autopilot[1] to wider adoption of voice assistance found on smartphones to diagnostics assistance for medical workers in medical imaging and more
