A Guide to Putting Together a Continuous ML Stack
Offered By: Toronto Machine Learning Series (TMLS) via YouTube
Course Description
Overview
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.
Syllabus
A Guide to Putting Together a Continuous ML Stack
Taught by
Toronto Machine Learning Series (TMLS)
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent