Thanks to SDV Enterprise by @datacebo's SDV Enterprise, ING Belgium can generate synthetic SEPA payments on-demand, ensuring thorough testing and compliance w/o compromising data privacy.
Learn more from @n4atki, our VP of Product, and Wim Blommaert, Head of Test Data Management at ING Belgium:
https://t.co/StylFinHku
Once you build a generative model to create #syntheticdata, you can sample “millions or even billions” of records of customers data, even if you train the model on just 10,000 customers' data, according to @kveeramac . “Enterprises can create a lot of data in their lower environments and not have to port the data from a large database, instead using this model that's maybe just a couple of gigabytes,” he says.
Check out @kveeramac full interview on the Cloudcast podcast w/Aaron Delp (@aarondelp ) & Brian Gracely (@bgracely), where he talks about how @datacebo is leading #syntheticdata efforts at: https://t.co/WZUgkOlNx8
#devops #bigdata #syntheticdata #generativeai #data #datascience #enterprisedata #tabulardata #predictiveAI #machinelearning #ML #generativemodels #MLmodels
We can't control the weather, but we ✨can✨ control our software: Instead of waiting for a storm, simulate a hypothetical one using #syntheticdata ⛈ ❄️
#SDV#datasimulation
https://t.co/6FBBn5foi3
In true #syntheticdata fashion, we’ve created some ✨synthetic✨ Season’s Greetings.
Happy holidays from the #sdv team and looking forward to a great 2023!
Have you ever looked at a GAN & wondered what's happening inside? 👀
We took a peek -- and made it more interpretable 🙌
#syntheticdata#sdv#CTGAN
https://t.co/AFcbUMxq3r
I started out my career as an AI researcher. Now, I'm leading product at a startup, convincing people that you don't always need the fanciest ML.
Irony ... or just the natural result of a business-driven mindset?
With AI and ML-powered coding tools on the rise, the data used to train these models are under scrutiny and raise major privacy concerns. We dig into how synthetic data presents a lower-risk solution that scales.
https://t.co/gmQ2rXAiyI
4 ways to compare synthetic & real data with SDMetrics.
Explore the open-source library: https://t.co/mQrSakt2dR
#SyntheticData#DS#DataScience#BigData
Having a solid product is good. What's better is to have a fast and reliable process for continuously improving it.
(This is why open-source has such an unfair advantage.)
Apple's iOS 16 "unsend" feature is a prisoner's dilemma:
1. If everyone upgrades, everyone gets to unsend 🙌
2. If I upgrade but you don't, you get to spy on my unsent texts 😡
3. If nobody upgrades, nobody gets to unsend 🤷🏽♀️
I'm not worried about AI taking over bc it just does what it's told. You need human creativity to caption that image: "me on a Monday, when my software isn't working"
For ML products, meta data is such a great tool for debugging. The user doesn’t have to share actual (private) data, but still communicates the structure/schema
#syntheticdata#machinelearning