AI training data can make or break your machine learning project. With data as the foundation, decisions on how much or how little data to use, methods of collection and annotation and efforts to avoid bias will directly impact the results of your machine learning models. In this guide, we address these and other fundamental considerations when embarking on an AI data project. Discover best practices for the sourcing, labeling and analyzing of training data from TELUS Digital, a leading provider of AI data solutions.
This guide covers:
- The different types of data used for training, testing and validation
- How much training data you need
- How to improve the quality of your data
- Where to get more training data
