A Guide to Putting Together a Continuous ML Stack

Explore a comprehensive guide to implementing the first level of MLOps maturity and performing continuous training of machine learning models through automated ML pipelines. In this 55-minute conference talk from the Toronto Machine Learning Series (TMLS), Software Engineer Kallie Levy from Superwise provides a hands-on dive into the process. Learn how to detect performance degradation and data drift, which can trigger the pipeline to create new models based on fresh data. Gain practical insights into building a robust continuous ML stack that enhances the efficiency and effectiveness of your machine learning operations.