🚀 Launching Synthetic Text to Unlock High-Value Proprietary Text Data

Read all about it here

Michael Platzer

Michael co-founded MOSTLY AI in 2017 and led the company as CEO until 2020. As Chief Strategy Officer he is responsible for the long-term vision and strategic development of the company. Michael is a world class data scientist who held leading positions at Microsoft and Nokia before founding MOSTLY AI. He was awarded with the Global Marketing Research Award by the American Marketing Association. He holds a PhD degree from the Vienna University of Economics and Business and a Master degree from the Vienna University of Technology. Michael is a proud dad of two daughters and passionate for all kinds of sports including running, biking and baseball.

Blogposts written by Michael

November 18, 2024

Michael Platzer

Differentially Private Synthetic Data with MOSTLY AI

MOSTLY AI now offers users the choice to train synthetic data generators with or without differential privacy (DP) guarantees! By toggling DP on in the model configuration, training becomes differentially private, and the platform tracks the privacy budget (Epsilon) throughout. Users can also set an upper Epsilon limit, stopping training automatically when the budget is […]

January 11, 2023

Michael Platzer

synthetic population data

Amp It Up: synthetic population data at scale

Large scale synthetic population data is as good as real for training machine learning models.

Michael Platzer

synthetic data prevents AI-based reidentification attacks

AI-based re-Identification attacks - and how to protect against them

A new type of privacy attack is emerging: using AI to re-identify people based on behavioral patterns. Old privacy tools no longer work, AI-generated synthetic data does.

Michael Platzer

Data annotation

How To Scale Up Your Text Annotation Initiatives with Synthetic Text

Training data needs to be annotated by humans in order to help the machines make sense of it. However, in just as many cases, that very same training data should not or must not be provided to humans due to its privacy -sensitive nature.

Michael Platzer

Fair AI and synthetic data

The State of Fair Synthetic Data in 2021

Any machine learning model that is trained on fair synthetic data will be representative of the real world but fair by design.

Michael Platzer

Synthetic geo data

Synthetic geo data with MOSTLY AI 1.5

MOSTLY AI now ships with geo data support, thus allowing any organization to synthesize and truly anonymize their geo enriched data assets at scale. In this blogpost we present examples of synthetic geo data using publicly available datasets.

Michael Platzer

Explainable AI with synthetic data for finance healthcare and public sector

The Future of Explainable AI rests upon Synthetic Data

AI governance requires access to models just as well as to data. However, sensitive training data cannot be shared further. MOSTLY AI’s highly representative synthetic data serves as a drop-in replacement to enable model documentation, model validation, as well as model certification.

November 4, 2020

Michael Platzer

Anonymous and accurate synthetic data

Truly anonymous synthetic data - evolving legal definitions and technologies (Part II)

This is the second part of our blog post series on truly anonymous synthetic data. While Part I introduced the fundamental challenge of true anonymization, this part will detail the technical possibilities of establishing the privacy and thus safety of synthetic data.

November 4, 2020

Michael Platzer

Anonymous and accurate synthetic data

Truly anonymous synthetic data - evolving legal definitions and technologies (Part I)

In this two-part series, we are taking a look at how data privacy legislation evolved and explore how technology can keep up with the demands of regulation.

1 2 Next »

Ready to start?

Sign up for free or contact our sales team to schedule a demo.

Get started free Request a demo