A Training Data Platform (TDP) is an investment in your models' maturity, accuracy, and endurance — so choosing one from the available options can be a difficult decision, especially with taking your use cases, stakeholders, existing processes, and future goals into account.
That's why we've created a comprehensive checklist of features and capabilities that a TDP should have to best serve your specific requirements as your ML needs scale. The list includes:
Annotation features such as an intuitive editor, collaboration tools, and configurable editors
Management tools such as real-time queueing, advanced workflows, and security features
Iteration capabilities such as quality management workflows, analytics, and labeling automation