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