How to upgrade your training data quality

What makes a good dataset for machine learning? As data-centric AI continues to gain traction among teams across industries, the task of producing high-quality training data for machine learning is increasingly prioritized.

Having worked with hundreds of leading AI enterprises, we've seen firsthand how machine learning teams have successfully enhanced and optimized their data annotation pipeline with quality management methods, updated ontologies, and better collaboration. In this ebook, you’ll discover the latest methods for improving labeled data quality, which will in turn boost model performance.

Read this guide to learn how to:

• Improve training data quality for machine learning with an iterative labeling approach

• Create the right ontology for your use case and optimize your labeling workflows

• Identify and correct labeling errors with QA workflows

• Use your model to improve training data quality for AI via model-error analysis