Abstract: 

Machine learning models are never done. The world is always changing and models
rely on data to learn useful information about this world. In ML systems we need to be able to
embrace change without sacrificing reliability. But how do we do it? MLOps.
MLOps, the process of operationalizing your machine learning technology, is fundamental to
any organization leveraging AI. However, the complexities of machine learning require
managing two lifecycles: the code and the data. Pachyderm is a platform that provides the
foundation for unifying these two lifecycles. In this session, you will learn how to manage
constantly changing data through versioning, unify data and code lifecycles, and institute data-
driven automation.

Bio: 

Jimmy Whitaker is the Data Science Evangelist at Pachyderm. He focuses on creating a great data science experience and sharing best practices for how to use Pachyderm. When he isn’t at work, he’s either playing music or trying to learn something new, because “You suddenly understand something you’ve understood all your life, but in a new way.