A machine learning algorithm isn’t worth much without great training data to power it. At Figure Eight, we’ve been providing that training data for a decade. We understand how to take raw data and annotate and label it so that it can be used to power the most ambitious projects for some of the most innovative companies in the world. Our Human-in-the-Loop AI platform transforms unstructured data from the real world–whether it’s text, images, audio or video–into high-quality large scale structured training datasets.
In this guide, we’ll give you a few of the lessons we’ve learned along the way so you can create the training data that makes your machine learning initiatives a success. You’ll learn:
- Why simply using more data is often better than finding the latest cutting edge algorithm
- Why just 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