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.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
In-context learning
As users increasingly rely on Large Language Models (LLMs) to accomplish their daily tasks, their concerns about the potential leakage of private data by these models have surged.
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English to French Translation:
Q: A bartender had 20 pints. One customer has broken one pint, another has broken 5 pints. A bartender boughtthree boxes, 4 pints in each. How many pints does bartender have now?
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Lorem ipsum dolor sit amet, Q: I had 10 cookies. I ate 2 of them, and then I gave 5 of them to my friend. My grandma gave me another 2boxes of cookies, with 2 cookies inside each box. How many cookies do I have now?
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A: At the beginning there was 10 cookies, then 2 of them were eaten, so 8 cookies were left. Then 5 cookieswere given toa friend, so 3 cookies were left. 3 cookies + 2 boxes of 2 cookies (4 cookies) = 7 cookies. Youhave 7 cookies.
English to French Translation:
Q: A bartender had 20 pints. One customer has broken one pint, another has broken 5 pints. A bartender boughtthree boxes, 4 pints in each. How many pints does bartender have now?
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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