The global machine learning (ML) market is expected to reach nearly $20 billion by 2023, across a growing number of industries including retail, automotive, medical, financial, and manufacturing. But while the opportunities to drive business value are vast, ML models require massive volumes of high-quality labeled data to learn about the problem they’re solving.
Contextually-labeled data is not only critical for selecting and training a model—it’s required for long-term success in production. Because the labeled data impacts model performance at every stage of its lifecycle, high-quality, contextually labeled data also represents a source of competitive advantage for ML initiatives. That's why it’s absolutely necessary data teams understand how to evaluate quality on an ongoing basis.
Alegion's Data Science Team put together a whitepaper on annotation guidelines and improving the quality of labeling data. Download a complimentary copy.
This guide looks at common industry metrics used to evaluate the quality of labeled data and explains how to establish quality requirements for your use case. It also provides guidance on maintaining and improving the quality of your data as your project scales.