The Gretel Epoch #8
Hi everyone, I’m thrilled to share the latest on Gretel’s products, research, and community. It’s been an exciting six months since our GA launch, and we want to share some of our learnings with you.
First, we are seeing an incredible pace of AI adoption in the health sciences and technology sector. As a result, the need for high-quality and private data is increasing dramatically. Popular use cases include data augmentation in research, boosting underrepresented datasets for clinical trials, and safe data sharing in the consumer health space. A common denominator driving innovation here is the ability to safely share and collaborate on data. That theme is driving demand for synthetic data, too, and has been a core focus for our team.
Another area we are investing heavily in is data simulation and how synthetics can help power and improve its utility. Many companies, particularly late-stage startups, are struggling with the “cold-start” problem, where the economic hurdles to data acquisition make it difficult, if not impossible, to launch and scale AI initiatives. However, synthetic data is helping bridge this gap and is supporting some fascinating multi-modal applications. Considering that a substantial portion of emerging products in this space are “first of,” the ability to simulate and create a wide range of real-world narratives, using multi-modal synthetic data, is very valuable.
If you’re working in any of these fields and want to learn more about how Gretel can help you, you can schedule a demo with our team.
Gretel Products & Research
- Generate synthetic data in 3 lines of code — how to create synthetic data for anything from a simple CSV to a large-scale, complex dataset with millions of rows of data and thousands of columns.
- The Evolution of Gretel’s Developer Stack for Synthetic Data — an introduction to our engineering philosophy and three pillars for building a developer stack for synthetic data that anyone can use.
- Measure the Quality of any Synthetic Dataset — how to assess the efficacy and quality of synthetic data with Gretel’s new Evaluate API.
- Safely Working with Another Company’s Data — an example of how synthetics can enable data sharing while reducing the risk of privacy-compromising linkage attacks.
- Test Data Generation — a brief history, from building products with “dummy” data and risky production subsets to today’s emerging gold standard: high-quality synthetics that can support the development of state-of-the-art AI/ML models.
Gretel Community
- Gretel Livestreams — this month, we hosted two developer workshops: an Intro to Synthetic Data and another on generating synthetic text with Gretel GPT. We also did a deep dive on our new Gretel Evaluate API and the art of Synthetic Data Quality Scoring.
- Getting Started with Synthetic Generation — we recently published a curated list of our best resources on the core concepts of synthetic data generation. If you’d like to learn more about other research on generative AI tools and privacy-enhancing techniques, check out this GitHub repo.
On behalf of the entire team, thanks for your support, and have fun synthesizing.
Ali Golshan
Co-founder/CEO
