Chapter 2. Training Data Concepts

Schema Deep Dive Introduction

What do you want your AI system to do? How will it accomplish it? What methods are you going to use? In this chapter I dive into some of the foundational concepts around supervised training data.

The real world is messy. And often commercial applications require a level of detail that’s far more specific then high level labels. There are many ways to structure this. In general these concepts exist as “pivot points” in the application.

I will introduce the core concepts around Training Data Schema - a paradigm for encoding Who, What, Where, How & Why. The Schema is the overall representation of Labels, Attributes, their Relation to each other. And more. It’s how we represent the meaning of what something is, where it is and more.

Info box?: Schema is also known as Ontology or Label Setup.

This builds on the high level concepts of Labels and Attributes ...

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