There is nothing more important to the success of your machine learning initiatives than acquiring contextually labeled, high quality training data.
You need expertise to arrive at quality training data, in addition to the right tools and technology. Developing the capacity to annotate massive volumes of data while maintaining quality is a function of the model development lifecycle that enterprises often underestimate. It’s resource intensive and requires specialized expertise. Your data science team needs partners and platforms it can trust to deliver the data quality you need.
At Alegion, we’ve established quality management best practices based on our experience labeling tens of millions of images, video, text, and audio records. In this guide, we explain the fundamental steps to quality data and how the Alegion team partners with you at each stage to ensure the quality you need.