alig90s

Looks pretty awesome. Have been waiting for something like this :)

With this release, the Gretel service enables creating synthetic data, transforming sensitive data, and classification workloads without having to write a single line of code

With this product release, the Gretel service enables the following workloads without having to write a single line of code.

Beta2 for Gretel.ai is all about delivering privacy engineering as a service through clean, simple APIs.

Beta2 for Gretel.ai is all about delivering privacy engineering as a service through clean, simple APIs.

A step-by-step guide to creating high quality synthetic time-series datasets with Python

In this blueprint we use docker to start up a local Elasticsearch cluster

Gretel's SDK includes detailed reporting that shows you how accurate your synthetic data's statistical distributions and correlations are.

The cornerstones of modern privacy regulations (such as Europe’s GDPR, and California’s CCPA), and also privacy-enhancing technologies such as differential privacy and k-anonymity, are centered on protecting the privacy of individual users within a dataset.

Use synthetic data and to improve model accuracy for fraud, cyber security, or any classification with an extremely limited minority class.

One of the biggest benefits to Gretel’s synthetic data library is its ability to automatically learn and maintain correlations and statistical relationships between columns at both the dataset and record levels.

A synthetics library that works directly with Pandas DataFrames and allows batched training of columns to exchange your source DataFrame directly with a synthetic DataFrame of the same shape.

Kubernetes Security Wiki, where we examine some of the most critical security-related topics in the world of containers, Kubernetes, and cloud-native application development.

Using FastText embeddings of field headers to improve our NLP for structured data. NLP can be tough in a structured environment as field values are often single words or numbers.

Sharing data safely is one of the biggest challenges in the healthcare industry today. For hospitals and health organizations, being able to compare and contrast new patient data with other medical organizations in their area and across the world can help doctors quickly diagnose patients and provide the best treatment possible.