Learn essential techniques for detecting and protecting sensitive healthcare information subject to privacy regulations like GDPR, PII, and HIPAA through a 39-minute video demonstration. Master the implementation of Microsoft Presidio for anonymizing DICOM medical images, while exploring complementary C# tools for handling DICOM and FHIR data formats. Access practical code examples through the provided GitHub repository to implement robust health data anonymization solutions in medical and healthcare environments.
MLOps Tools for Health Data Anonymization in Healthcare and Medical Environments
The Machine Learning Engineer via YouTube
Overview
Syllabus
MLOPS: Tools for Health Data Anonymization #datascience #machinelearning
Taught by
The Machine Learning Engineer
Related Courses
-
MLOps: Herramientas para Detectar y Proteger Datos Médicos
-
Get started with Azure Health Data Services
-
PII/GDPR Detection with Presidio and Spacy - Part 1: Architecture and Design
-
AI-Driven Solutions for Text Anonymization - Implementing Machine Learning for Data Privacy
-
Accelerating Public Health Research: Why Healthcare Needs MLOps
-
How MLOps Tools Will Need to Adapt to Responsible and Ethical AI - Stay Ahead of the Curve
