EPISODE · Jul 11, 2020 · 55 MIN
MLOps Meetup #23 // Monitoring the ML Stack // Lina Weichbrodt
from MLOps.community · host Demetrios
Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterHow To Monitor Machine Learning Stacks - Why Current Monitoring is Unable to Detect Serious Issues and What to Do About It with Lina Weichbrodt. Monitoring usually focuses 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 suggest 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. 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. Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Lina on LinkedIn: https://www.linkedin.com/in/lina-weichbrodt-344a066a/
What this episode covers
Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterHow To Monitor Machine Learning Stacks - Why Current Monitoring is Unable to Detect Serious Issues and What to Do About It with Lina Weichbrodt. Monitoring usually focuses 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 suggest 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. 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. Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Lina on LinkedIn: https://www.linkedin.com/in/lina-weichbrodt-344a066a/
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MLOps Meetup #23 // Monitoring the ML Stack // Lina Weichbrodt
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