The Nuances of AI Testing: Learnings from AI red-teaming
Artificial Intelligence (AI) Testing is a complex field that transcends the boundaries of traditional performance testing. While AI developers are well-versed with performance testing due to its prevalence in the educational system, it is crucial to understand that AI encompasses much more than just performance. In this post, I’d like to list some key principles…
Towards Quality Assurance in Machine Learning
I had a chance to attend the PyConDE & PyData Berlin event this year where I gave a talk on machine learning (ML) testing and validation. Now the recording is available on YouTube and if you’re interested in how to bring “quality management” in machine learning pipeline, you may find the talk interesting. I also…
Model Validation and Monitoring: New phases in the ML lifecycle
Validation/testing and monitoring of the ML models might be a luxury in the past. But with the enforcement of the regulations on artificial intelligence, they are now indispensable parts of the machine learning pipeline. In the last decade, machine learning (ML) research and practice have gone a long way in establishing a common framework in designing systems and applications…