ML System Design for Continuous Experimentation

To access this webinar, please register here: https://hubs.li/Q0112D3r0

Topic: ML System Design for Continuous Experimentation

Speaker: Harpreet Sahota, Data Scientist, Comet

Harpreet is a thought leader in the Data Science space and hosts The Artists of Data Science podcast

Abstract:

While ML model development is a challenging process, the management of these models becomes even more complex once they're in production.

Shifting data distributions, upstream pipeline failures, and model predictions impacting the very dataset they're trained on can create thorny feedback loops between development and production.
In this live webinar, we will

· Examine some naive ML workflows that don't take the development-production feedback loop into account and explore why they break down.

· Showcase some system design principles that will help manage these feedback loops more effectively.

· Explore several industry case studies where teams have applied these principles to their production ML systems.

[19th-21st April] Get 75% discount on you ODSC East 2022 conference - https://hubs.li/H0_t0tT0

ODSC Links:
• Get free access to more talks/trainings like this at AI+ Training platform:
https://aiplus.training/
• Facebook: https://www.facebook.com/OPENDATASCI
• Twitter: https://twitter.com/odsc & @odsc
• LinkedIn: https://www.linkedin.com/company/open-data-science
• Slack Channel: https://bit.ly/35pfPZo
• Code of conduct: https://odsc.com/code-of-conduct/