Lina Weichbrodt, ML Freelance and Consulting
Traditional software monitoring best practices are not enough to detect problems with machine learning stacks. How can you detect issues and be alerted in real-time? This talk will give you a practical guide on how to do machine learning monitoring: which metrics should you implement and in which order of priority? Can you use your team's existing monitoring and dashboard tools, or do you need an MLOps platform?
Lina Weichbrodt[node:field-speakers-institution]
Lina has 10+ years of industry experience in developing scalable machine-learning models and bringing them into production. She currently works as a pragmatic machine-learning freelancer and consultant. She has helped clients in e-commerce, fintech, mobility, and travel to get value out of their AI projects. She previously worked at Zalando developing real-time, deep-learning personalization models for more than 32M users.
author = {Lina Weichbrodt},
title = {Symptom-based Alerting for Machine Learning - What I Learned from Monitoring More than 30 Machine Learning Use Cases},
year = {2023},
address = {Dublin},
publisher = {USENIX Association},
month = oct
}
