5 Steps for Training and Testing AI Algorithms


AI adoption is rarely easy. With mountains of data and code to cover, it's hard to do alone. Here's a 5-step strategy to train and test the data for your AI algorithm.

Artificial intelligence and machine learning are taking us head-first into an increasingly digital world. AI/ML enables organizations to streamline processes, enabling them to move faster without relinquishing quality. However, successful algorithms require thorough training and testing data to produce reliable results and avoid bias.

The training and testing data will create the foundation for a successful AI algorithm and a reliable engine. This ebook reviews common challenges organizations face when training and testing your AI algorithm, and provides tips on how to avoid or manage them.

Read on to learn:

  • How to scale data for AI
  • Practices for testing bias
  • Tips for codifying data
  • How to get the most from your AI algorithm


Applause is the worldwide leader in crowdsourced digital quality testing. With testers available on-demand around the globe, Applause provides brands with a full suite of testing and feedback capabilities. This approach drastically improves testing coverage, eliminates the limitations of offshoring and traditional QA labs, and speeds up time-to-market.