This webinar focuses on how teams can leverage unstructured data, including text across any file type (including PDFs) and audio – while protecting privacy – in order to fine-tune ML models. Watch for best practices and real world examples from Tonic Textual customers.
Join us as we dive into key de-identification strategies, including targeted redaction and the creation of high-fidelity synthetic datasets that preserve statistical utility while protecting individual privacy. We’ll walk through how these methods can support HIPAA, GDPR, and other compliance requirements; seamlessly integrate into downstream ML workflows.
You’ll learn from real-world examples of how leading organizations are using Tonic Textual to unlock the power of their unstructured data—without compromising privacy. Whether you're building healthcare models or customer-facing chatbots, this session will give you practical insights into making unstructured data safe, compliant, and ML-ready.
Who should attend:
Data scientists and leads, ML engineers and managers, compliance officers, and technical leaders focused on responsible AI development.