Model Monitoring in Practice: Top Trends
Speakers
Krishnaram Kenthapadi is the Chief Scientist of Fiddler AI, an enterprise startup building a responsible AI and ML monitoring platform. Previously, he was a Principal Scientist at Amazon AWS AI, where he led the fairness, explainability, privacy, and model understanding initiatives in the Amazon AI platform. Prior to joining Amazon, he led similar efforts at the LinkedIn AI team and served as LinkedIn’s representative on Microsoft’s AI and Ethics in Engineering and Research (AETHER) Advisory Board. Previously, he was a Researcher at Microsoft Research Silicon Valley Lab. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006. He serves regularly on the program committees of KDD, WWW, WSDM, and related conferences, and co-chaired the 2014 ACM Symposium on Computing for Development. His work has been recognized through awards at NAACL, WWW, SODA, CIKM, ICML AutoML workshop, and Microsoft’s AI/ML conference (MLADS). He has published 50+ papers, with 4500+ citations and filed 150+ patents (70 granted). He has presented tutorials on privacy, fairness, explainable AI, and responsible AI at forums such as KDD ’18 ’19, WSDM ’19, WWW ’19 ’20 '21, FAccT ’20 '21, AAAI ’20 '21, and ICML '21.
At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.
Mihail is a co-CEO of Storia AI, an early-stage startup building an AI-powered creative assistant for video production. He has over a decade of experience researching and engineering AI systems at scale. Previously he built the first deep-learning dialogue systems at the Stanford NLP group. He was also a founding member of Amazon Alexa’s first special projects team where he built the organization’s earliest large language models. Mihail is a serial entrepreneur who previously founded Confetti AI, a machine-learning education company that he led until its acquisition in 2022.
SUMMARY
We first motivate the need for ML model monitoring, as part of a broader AI model governance and responsible AI framework, and provide a roadmap for thinking about model monitoring in practice.
We then present findings and insights on model monitoring in practice based on interviews with various ML practitioners spanning domains such as financial services, healthcare, hiring, online retail, computational advertising, and conversational assistants.
CONTENT & TRANSCRIPT
"The scientist is not a person who gives the right answers, he is one who asks the right questions." - Claude Levi-Strauss
The Power of Questions
From "Why should we care?" to realizing the importance of monitoring ML in production
The AI Hierarchy of Needs
"Things start with asking the right questions"
- Are we collecting the correct representative data?
- Do we need to measure biases?
- Is the model having similar predict behavior across different user groups?
- Is the model starting to degrade in performance when in production?
- To what extent do we as humans want an explanation for every prediction?
- To what extent are we comfortable trusting one model if we don't understand how they are working?
- How we as humans are shaping our views of these new changes?
The ethical side of AI
- Is the data you're using sourced ethically
- Were the people aware of the purposes of their labor?
- Were they paid fairly?
- Could the answers to these questions affect the quality of the datasets and how?
- How would that affect your ML-powered product and end-users?