Workshop: A Guide to Putting Together a Continuous ML Stack
We’ll take a hands-on dive into implementing the 1st level of MLOps maturity and performing continuous training of the model by automating our ML pipeline. We’ll start with the ML pipeline and see how we can detect performance degradation and data drift in order to trigger the pipeline and create a new model based on fresh data.
Continuous MLOps pipelines: A dive into continuous training automation
In this webinar, we’ll learn how to implement the 1st level of MLOps maturity and perform continuous training of the model by automating the ML pipeline. We'll start with the ML pipeline and see how we can detect performance degradation and data drift in order to trigger the pipeline and create a new model based on fresh data.
This session will cover architectural considerations for multi-tenancy in ML, best practices in traditional software engineering that can be copy/pasted over to MLOps, as well as new considerations unique to ML
