Tecton
timezone
+00:00 GMT
SIGN IN
  • Home
  • Events
  • Content
  • Help
Sign In
Sign in or Join the community to continue

Machine Learning in Production: What I learned from monitoring 30+ models

Posted Apr 12, 2022 | Views 740
# Explainability and Observability
# Production Use Case
Share
SPEAKER
Lina Weichbrodt
Lina Weichbrodt
Lina Weichbrodt
Machine Learning Lead Engineer @ DKB AG

Lina has 10+ years of experience developing scalable machine learning models and running them in production. She currently works as a Machine Learning Lead Engineer at the DKB bank. She previously worked at Zalando developing real-time, deep learning personalization models for more than 32M users.

+ Read More

Lina has 10+ years of experience developing scalable machine learning models and running them in production. She currently works as a Machine Learning Lead Engineer at the DKB bank. She previously worked at Zalando developing real-time, deep learning personalization models for more than 32M users.

+ Read More
SUMMARY

It's a software monitoring best practice to alert on symptoms, not on causes. "Customer Order Rate dropped to 0" is a great alert: it alerts directly on a bad outcome. For machine learning stacks, this means we should focus monitoring on the output of our models. Data monitoring is also helpful, but should come later in your maturity cycle. In this talk, I will provide practical strategies for prioritizing your monitoring efforts.

+ Read More

Watch More

1:00
Workshop: Bring Your Models to Production with Ray Serve
Posted Jun 01, 2022 | Views 934
# Data engineering
# Model serving
# Systems and Architecture
15
Why is Machine Learning Hard?
Posted Apr 12, 2022 | Views 692
# Data engineering
# Organization and Processes
# Production Use Case
# Systems and Architecture
10
Data Observability for Machine Learning Teams
Posted Apr 12, 2022 | Views 938
# Data engineering
# Explainability and Observability