Abstract: 

Data-centric AI is bridging the gap between research and practice. Instead of optimizing our algorithms and architectures, pivoting to focus on data as the primary way to improve our machine learning models is yielding tremendous results. But this shift to data has left some gaps in our development process, and with this shift, we need to rethink how we develop AI from tooling to processes.

Session Outline:

In this presentation join us as we examine:
● Data Centric AI and how did we get here?
● Data as the new ‘Source Code’
● What are the practical steps towards Data Centric AI

Bio: 

Jimmy Whitaker is the Chief Scientist of AI 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.”