NannyML — OSS Python library for detecting silent ML model failure

Description

“ Hey Everyone! I am Hakim, co-founder of NannyML. Early in my data science career, I developed the intuition that machine learning models are dynamic systems. You set performance metrics, train them on historical data, put them out into the real world to make decisions, and then the real-world changes. So what happens to these models and their performance? For the most part, we have no idea. There is no way to measure the performance of most machine learning models directly. Ground truth is delayed or doesn't exist. We developed an algorithm called Confidence-based Performance estimation (CPBE). It is the only open-source algorithm capable of fully capturing the impact of data drift on performance. We also have some extra features to help you understand why it has happened. Our core algorithms are free and open-source forever. Stop by (https://github.com/NannyML/nanny.... We would love feedback from you guys on how you currently solve this problem in your workflows, and we can't wait to continue building this product with the community. ” – Hakim Elakhrass Discussion | Link