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Demo: fine-tuning LLMs with Tonic Textual

In this blog post, Tonic.ai’s Head of AI, Ander Steele, walks through a live demo of how Tonic Textual can be used to automatically de-identify protected health information (PHI) within unstructured data—making it safe and compliant for fine-tuning large language models (LLMs).

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Data masking vs encryption: what’s the best way to protect my data?

Data de-identification
Data de-identification
Data privacy
Test data management
Tonic Structural

What is data subsetting? The art and science of only using the data you need

Test data management
Test data management
Data privacy
Tonic Structural

How to create realistic test data for Amazon Redshift

Test data management
Test data management
Data de-identification
Tonic Structural

How does differential privacy work? Plus, how to know if your organization needs it!

Data privacy
Data privacy
Tonic Structural

What is patient data privacy in healthcare? Everything you need to know

Healthcare
Healthcare
Tonic Structural

What is the role of data synthesis in my CI/CD pipeline, anyway?

Test data management
Test data management
Data de-identification
Tonic Structural
Tonic Fabricate

How to integrate Tonic.ai into your CI/CD pipeline with Jenkins

Technical deep dive
Technical deep dive
Tonic Structural

Creating fake data for testing in Snowflake? You should be using Tonic. Here’s why.

Product updates
Product updates
Tonic Structural

How to mask data for testing in MongoDB

Technical deep dive
Technical deep dive
Tonic Structural

Four ways to generate fake data that aren’t working as well as you think

Test data management
Test data management
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Anonymizing your data in Db2 for better testing and development

Technical deep dive
Technical deep dive
Tonic Structural

We raised a $35M Series B. Here's what's next for fake data.

Tonic.ai editorial
Tonic.ai editorial
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