Essential Guide to Training Data

There's a saying of garbage in, garbage out. It’s common knowledge that every machine learning solution needs a good algorithm powering it, but what gets far less press is what actually goes into these algorithms: the training data itself. Your model is only as good as the data it's trained on.

Pulling from over 20 years of experience, we break down how to approach training data, starting with raw data and annotating and labeling it so that it can be used to power the most ambitious projects. It's how we help some of the most innovative companies in the world. This guide will give you a few of the lessons we’ve learned along the way.

In the Essential Guide to Training Data we’ll cover everything you need to know about creating the training data necessary to drive successful machine learning projects, including:

  • Why having a lot of big data isn’t the same as having labeled data 
  • How to determine which labels to use to evaluate your success 
  • Where to find some great open datasets to bootstrap your model

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