Solving Data Quality with ML Observability and Data Operations

The article discusses the importance of maintaining high-quality data for machine learning (ML) models and how modern MLOps solutions need to address both code and data aspects. It highlights that ensuring good data quality is a continuous process, requiring ongoing investment. The article delves into the key dimensions of data quality, which include accuracy, completeness, consistency, privacy and security, up-to-dateness, relevance, reliability, timeliness, usability, and validity. It further explores how these dimensions can be addressed for structured and unstructured data using ML observability and Data Operations platforms respectively. The article concludes by emphasizing the benefits of investing in data quality management for unlocking the potential of an organization's structured and unstructured data.