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Drift Happens, featuring Yunshi Zhao of Liftoff

https://arize.zoom.us/j/82831610497

Join us Thursday, May 12th, 4pm ET/ 1pm PT for Drift Happens, Arize’s weekly video chat where our host Amber Roberts will discuss ML use cases, best practices in model development and model deployment, as well as troubleshooting models in production with industry’s top MLEs and Data Scientists. Ceci is a Machine Learning Engineer at...

apply(conf)

Virtual , United States

Drift Happens featuring Mike Stefferson of Cerebral

https://arize.zoom.us/j/82831610497

Join us Thursday, May 19th, 4pm ET/ 1pm PT for Drift Happens, Arize’s weekly video chat where our host Amber Roberts will discuss ML use cases, best practices in model development and model deployment, as well as troubleshooting models in production with industry’s top MLEs and Data Scientists. This week we will be joined by...

Drift Happens featuring Chris Murphy of Homepoint

https://arize.zoom.us/j/82831610497

Join us Thursday, May26th, 4pm ET/ 1pm PT for Drift Happens, Arize’s weekly video chat where our host Amber Roberts will discuss ML use cases, best practices in model development and model deployment, as well as troubleshooting models in production with industry’s top MLEs and Data Scientists. This week on Drift Happens we are joined...

Drift Happens featuring Sid Roy of Devron

https://arize.zoom.us/j/82831610497

Join us Thursday, June 2nd, 4pm ET/ 1pm PT for Drift Happens, Arize’s weekly video chat where our host Amber Roberts will discuss ML use cases, best practices in model development and model deployment, as well as troubleshooting models in production with industry’s top MLEs and Data Scientists. Sid Roy is currently working as a...

MLOps World

Toronto, Canada Toronto, Canada

MLOps Day 2 Summit: Monitor, Observe, Explain

Virtual , United States

So you deployed a model. Now what? The AI Infrastructure Alliance’s Day 2 Summit gives you the answers you need to keep those models running smoothly. Hear from the top platforms dedicated to the world of Machine Learning Monitoring, Observability and Explainability