The world of data that we’ve lived in for the last 30 years is almost irrelevant when it comes to Machine Learning (ML or AI). Data science is a deepening field that is becoming ever more important to enterprises today. ML systems consume data; usually a lot of it. The concepts involved and terminology are intimidating and not very accessible to most of us. Enabling intelligence in your automation platform requires predictive and prescriptive data models (ML) that operate on the right data. Knowing what kind of data they need to consume to produce useful results is critical. You don’t need to be a data scientist to be able to grasp why this data is different.
Recently, I was joined by industry experts from Cognitive Scale to discuss how to think about data in a new way and give real world examples of how these things have played out in actual ML projects. Listen in to gain an understanding of how to write a shopping list to feed your ML beasts a healthy diet.