Скачать или смотреть Understanding Data Types, Anonymized Data, and Synthetic Data

This podcast explains the key differences between structured, semi-structured, and unstructured data. Structured data, like that found in databases, is highly organized and easily analyzed. Semi-structured data, such as JSON files, has some organization but lacks a rigid schema. Unstructured data, including images and videos, is completely disorganized and requires advanced analytical techniques. The podcast also highlights differences in data storage, analysis methods, and the rapid growth of unstructured data. Understanding these distinctions is crucial for effective data management.

Additionally, this podcast discusses the difficulties in obtaining and utilizing Real-World Data (RWD) for machine learning, primarily due to stringent regulations like HIPAA. Data anonymization, which involves removing all Protected Health Information (PHI) and Personally Identifiable Information (PII), is crucial but adds significant costs, including the expense of specialized software and expert labor. Furthermore, the high cost of expert annotation and labeling of medical data presents another obstacle to accessing and preparing RWD for model training, highlighting the gap between the abundance of raw data and its practical accessibility for research.

Furthermore, this podcast explains the use of synthetic data—artificially generated data—in machine learning. It highlights how generative AI (GAI) creates synthetic data mimicking real-world data types (text, images, etc.), addressing crucial issues like missing data imputation and data augmentation for small datasets. The benefits include improved model accuracy, better handling of imbalanced data, and enabling training where real data is scarce or sensitive. Ultimately, synthetic data optimizes the machine learning lifecycle by improving both data quality and quantity.