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We explore the popular open-source package Optuna to demonstrate how you can optimize your model hyperparameters and build the best synthetic model possible.
This article clarifies some common misconceptions about differential privacy and what it guarantees.
Quickly and safely aggregate geolocation data for location density analysis using a hexagonal grid system.
Use Gretel’s NLP setting to label PII including people names and geographic locations in free text.
Enabling faster access to data for medical research with statistically accurate, equitable and private synthetic datasets.
Today we announced that Gretel raised $50 million in funding to help us advance our mission to bring “privacy by design” to all developers.
By merging breakthrough research on text metrics with new types of embeddings, we produce a reliable metric that is highly correlated with human ratings.
There is a lot of hype around NLP. In this post, we explore some of the criticisms and how you can use this technology responsibly.
Create synthetic data that’s safer than ever. Our simple configuration file settings enable you to secure both your data and model from adversarial attacks.
In this blog, we will use Fluent Bit to collect logs from AWS EKS cluster applications.
In this blog post, we build an ETL pipeline that generates synthetic data from a PostgreSQL database using Gretel’s Synthetic Data APIs and Apache Airflow.
Run privacy engineering workloads at the touch of an API call.
Beta2 for is all about delivering privacy engineering as a service through clean, simple APIs.
I joined Gretel because of the opportunity, people, and problem.
In this post, we will dive into what privacy engineering is, why it’s important, and some of the core use cases we are seeing that are enabled by privacy.
Pandas provides so many options of reading data into a DataFrame, here's our short guide to ones that we found most useful.
Our new configuration templates will help you pick some of the right parameters needed to train your synthetic data models.
Implementing a practical attack to measure un-intended memorization in synthetic data models.
A game-changing AI that will disrupt the cocktail industry and spin the world on its head.
Anonymize data at access time with Gretel and Amazon S3 Object Lambda.
Gretel’s new synthetic report is here, featuring a high-level score and metrics to help you assess the quality of your synthetic data.
Smart-seeding lets you train a synthetic data model to auto-complete partial records and text.
Can synthetic data really be used in machine learning? We explore the utility of synthetic data created from popular datasets and tested on popular ML algorithms.
Here's what we learned about privacy engineering from 50+ companies and hundreds of developers.
A step-by-step guide to creating high quality synthetic time-series datasets with Python.
Train an AI model to create an anonymized version of your dataset using Python, Pandas, and gretel-synthetics.
What if we could ensure that personal data was protected, benefiting not just the individual but also giving developers faster, worry-free access to data?
Set up a cutting edge environment for deep learning with TensorFlow 2.4 and GPU support.
Use's APIs to continuously detect and protect sensitive data including credit cards, credentials, names, and addresses.
Create a fair, balanced, privacy preserving version of the 1994 US Census dataset using gretel-synthetics.
A practical guide to creating differentially private, synthetic data with Python and TensorFlow.
We are pleased to share that Gretel raised $12M in Series A funding. We're picking up strong momentum in our mission to help developers create safe data.
Create a simple workflow to perform Named Entity Recognition (NER) on sample data using Gretel and load the records into Elasticsearch.
We are releasing new features that make working with data easier by helping you deep dive into records, use blueprints to auto-anonymize data, and more.
In this post, we walk through building a data pipeline that will automatically transform datasets so they can be safely used in development environments.
We are launching Gretel Blueprints, making it easy to anonymize and balance datasets with just a few clicks.
Gretel's Premium SDK now includes detailed reporting that shows you how accurate your synthetic data's statistical distributions and correlations are.
Create differentially private, synthetic versions of datasets and meet compliance requirements to keep sensitive data within your approved environment.
We recently launched our new entity stream view in Gretel Cloud. See how you can view record streams from tagged entities in your data projects.
Build differentially private synthetic datasets in Python.
Learn how we are improving our product by adding new features that make connecting to Gretel easier, faster and more streamlined.
We founded Gretel based on our beliefs that data shouldn’t be scary.
Use synthetic data and to improve model accuracy for fraud, cyber security, or any classification with an extremely limited minority class.
Generate artificial records to balance biased datasets and improve overall model accuracy.
Explore how to create a batch interface with the latest version of Gretel Synthetics on Google Colaboratory.
Gretel Outpost is a free integration architecture that automates the steps that a security team would take in assessing the risk or exposure to data.
We decided to examine the privacy preserving capabilities of the Contact Tracing proposal, how it would be implemented, and what privacy concerns exist.
Use Gretel Synthetics and Colaboratory’s free GPUs to train a model to automatically generate fake, anonymized data with differential privacy guarantees.
Learn more about how Gretel's REST APIs automatically build a metastore that makes it easy to understand what is inside of your data.
We’re going to train and build our synthetic dataset off of a real-time public feed of e-bike ride-share data called the GBFS (General Bike-share Feed)
At Gretel, we realized that we can apply machine learning, synthetic data, and formal reasoning to offer provable privacy guarantees for data.
Take a deep dive on training Gretel’s open-source, synthetic data library to generate electronic health records that protect individual privacy (PII).
Look at how FastText word embeddings can help to quickly understand new datasets, and build more consistent labels for your own data.
Learn about new features in Gretel, and how those features enabled us to discover personally identifiable information (PII) in a popular Kaggle dataset.
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