Testing machine learning systems is currently more of an art form than a standardized engineering practice. This is particularly problematic for machine learning in mission-critical contexts. This article summarizes three steps from our ML testing series that any development team can take when testing their ML systems.
Testing machine learning (ML) systems is currently more of an art form than a standardized engineering practice. This is particularly problematic for machine learning in mission-critical contexts. In these use cases, strict performance guarantees and regulatory compliance are a must. The best engineering teams at companies like Tesla have built sophisticated testing infrastructure to ensure the reliability of their ML systems. Now it is time to make effective and systematic ML testing a reality for the rest of us–including smaller engineering teams–as well.
This article summarizes three steps from our ML testing series that any development team can take when testing their ML systems:
Adopting these three strategies is the first step to making ML testing more systematic and effective. They often provide a high return on investment. This is the case especially for smaller engineering teams that don’t have Tesla’s resources but that require strict performance guarantees and want to move through product development quickly and efficiently.
Lakera’s validation engine, MLTest, finds critical performance vulnerabilities in computer vision systems before they enter operation. Built with industry-leading AI and safety expertise, MLTest makes reliability a no-brainer for entire development teams. Get in touch if you want to learn more!
[1] Definition from ISO 21448: Road vehicles — Safety of the intended functionality.
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