Meetup #23

Monitoring the ML Stack

Monitoring usually focusses on the “four golden signals”: latency, errors, traffic, and saturation. Machine learning services can suffer from special types of problems that are hard to detect with these signals. The talk will introduce these problems with practical examples and suggests additional metrics that can be used to detect them. A case study demonstrates how these new metrics work for the recommendation stacks at Zalando, one of Europe’s largest fashion retailers.

In this episode

Lina  Weichbrodt

Lina Weichbrodt

Machine Learning Lead Engineer, DKB | Deutsche Kreditbank AG

Lina has 8+ years of industry experience in developing scalable machine learning models and bringing them into production. She currently works as the Machine Learning Lead Engineer in the data science group of the German online bank DKB. She previously worked at Zalando, one of Europe’s biggest online fashion retailers, where she developed real-time, deep learning personalization models for more than 32M users.

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Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.