🚀 MOSTLY AI releases World’s First Industry-Grade Open-Source Toolkit for Synthetic Data

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John Sullivan

Principal Consultant

Blogposts written by John

Maximizing the Potential of Databricks Clean Rooms with Synthetic Data

Fueled by the exponential growth in external data and AI for innovation, Databricks has introduced Clean Rooms to enable organizations to securely collaborate on sensitive data while maintaining privacy. Participants can share and join data, run complex workloads in any language, and retain full control of where and how that data is being used - […]

October 1, 2024

Creating privacy-preserving synthetic text in Databricks to safely fine-tune your custom LLM

TL;DR: Databricks research shows that 90% of businesses are holding back on GenAI due to concerns over accuracy, safety, and governance. MOSTLY AI helps you overcome these challenges by using privacy-preserving synthetic text to safely fine-tune your custom LLMs. Introduction Fine-tuning a model is the process of adapting a pre-trained LLM to instill specific knowledge […]

Data Access + Data Insights for Everyone

"Business people don't understand data". As a non data scientist, I've been told this quite a lot. And it's often true. The majority of people don't have the skills to work with data. To analyze data, to visualize data, to understand data. That's a problem, because the answers to many of your questions are in […]

August 10, 2023

Insurance innovation: 3 use cases powered by synthetic data in health insurance

Insurance innovation powered by synthetic data includes exciting use cases, such as data democratization, external data sharing, and empowering women in healthcare.

Personalizing the digital banking experience with synthetic data

How to win the digital transformation race? By personalizing the digital banking experience with synthetic data!

Data-driven personalization analyzes customer data in real time. Machine-learning models provide personalized services based on the customer's financial profile. These machine-learning models need quality data—and lots of it.

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