Examples

Use case based tutorials.

​Synthesize Tabular Data with Gretel Synthetics LSTM model
​Redact PII using Gretel Transforms
​Discover PII using Gretel Classify
Blueprint Notebooks
Synthetics
Notebook
Description
Description
​trainer-notebook 

Open in Colab​
This notebook is designed to help users successfully train synthetic models on complex datasets with high row and column counts. The code works by intelligently dividing a dataset into a set of smaller datasets of correlated columns that can be parallelized and then joined together.
​create-synthetic-data 
Open in Colab​
Walk through the basics of using Gretel's Python SDK to create a synthetic dataset from a Pandas DataFrame or CSV.
​create-synthetic-data-in-your-cloud​
Open in Colab​
Train a synthetic model locally and generate data in your environment.
​conditional-data-generation-example​
Open in Colab​
Conditional data generation (seeding a model) is helpful when you want to preserve some of the original row data (primary keys, dates, important categorical data) in synthetic datasets.
​balance-dataset-example​
Open in Colab​
Balance demographic representation bias in a healthcare set using conditional data generation with a synthetic model.
​create-synthetic-time-serie​s​
Open in Colab​
Create synthetic time-series data from a Pandas DataFrame or CSV.
​synthetic-oversampling-for-imbalanced-data​
Open in Colab​
Use a synthetic model to boost the representation of an extreme minority class in a dataset by incorporating features from nearest neighbors.
​time-series-poc 
Open in Colab​
Use Gretel APIs to anonymize, synthesize, and then compare synthetic accuracy for a time-series dataset vs real world data.
​parameter-sweep-wandb 
Open in Colab​
Run a sweep to automate hyper parameter optimization for a synthetic model using Weights and Biases.
​improve-accuracy-heart-disease 
Open in Colab​
Augment a popular machine learning dataset with synthetic data to improve downstream accuracy and algorithmic fairness.
​synthetic-data-with-differential-privacy​
Open in Colab​
Measure the effects of different differential privacy settings on a model's ability to memorize and replay secrets in a dataset.
​synthesize-a-postgres-database​
Open in Colab​
This notebook shows how to generate synthetic data directly from a multi-table relational database to support data augmentation and subsetting use cases.
​conditional-text-generation-with-gpt​
Open in Colab​
Generate realistic but synthetic text examples using an open-source implementation of the GPT-3 architecture.
​timeseries-with-DoppelGANger​
Open in Colab​
Generate synthetic daily oil price data using the DoppelGANger GAN for time-series data.
​evaluate-synthetic-data-quality​
Open in Colab​
Produce a quality score and detailed report for any synthetic dataset vs. real world data.
Transforms
Notebook
Launch
Description
​Redact PII​
Open in Colab​
In this blueprint, we will create a transform policy to identify and redact or replace PII with fake values. We will then use the SDK to transform a dataset and examine the results.
​Local Transform​
Open in Colab​
Label and transform sensitive data locally in your environment.
​De-identify Data​
Open in Colab​
In this deep dive, we will walk through some of the more advanced features to de-identify data with the Transform API, including bucketing, date shifts, masking, and entity replacements.
​Anonymize a Postgres Database​
Open in Colab​
This notebook walks through creating a policy using the Transform API to de-identify and anonymize data in a Postgres database for test use cases.
Classify
Notebook
Launch
Description
​Discover PII​
Open in Colab​
In this blueprint, we will create a classification policy to identify PII as well as a custom regular expression. We will then use the SDK to classify data and examine the results.
​Local Classify​
Open in Colab​
Label managed and custom data types locally in your environment.
​Classify Freetext Using NLP​
Open in Colab​
In this blueprint, we analyze and label a set of freetext email dumps looking for PII and other potentially sensitive information using NLP.

Quickstart

Synthesize Tabular Data

Last modified 3mo ago

Notebook	Description	Description
trainer-notebook	Open in Colab	This notebook is designed to help users successfully train synthetic models on complex datasets with high row and column counts. The code works by intelligently dividing a dataset into a set of smaller datasets of correlated columns that can be parallelized and then joined together.
create-synthetic-data	Open in Colab	Walk through the basics of using Gretel's Python SDK to create a synthetic dataset from a Pandas DataFrame or CSV.
create-synthetic-data-in-your-cloud	Open in Colab	Train a synthetic model locally and generate data in your environment.
conditional-data-generation-example	Open in Colab	Conditional data generation (seeding a model) is helpful when you want to preserve some of the original row data (primary keys, dates, important categorical data) in synthetic datasets.
balance-dataset-example	Open in Colab	Balance demographic representation bias in a healthcare set using conditional data generation with a synthetic model.
create-synthetic-time-series	Open in Colab	Create synthetic time-series data from a Pandas DataFrame or CSV.
synthetic-oversampling-for-imbalanced-data	Open in Colab	Use a synthetic model to boost the representation of an extreme minority class in a dataset by incorporating features from nearest neighbors.
time-series-poc	Open in Colab	Use Gretel APIs to anonymize, synthesize, and then compare synthetic accuracy for a time-series dataset vs real world data.
parameter-sweep-wandb	Open in Colab	Run a sweep to automate hyper parameter optimization for a synthetic model using Weights and Biases.
improve-accuracy-heart-disease	Open in Colab	Augment a popular machine learning dataset with synthetic data to improve downstream accuracy and algorithmic fairness.
synthetic-data-with-differential-privacy	Open in Colab	Measure the effects of different differential privacy settings on a model's ability to memorize and replay secrets in a dataset.
synthesize-a-postgres-database	Open in Colab	This notebook shows how to generate synthetic data directly from a multi-table relational database to support data augmentation and subsetting use cases.
conditional-text-generation-with-gpt	Open in Colab	Generate realistic but synthetic text examples using an open-source implementation of the GPT-3 architecture.
timeseries-with-DoppelGANger	Open in Colab	Generate synthetic daily oil price data using the DoppelGANger GAN for time-series data.
evaluate-synthetic-data-quality	Open in Colab	Produce a quality score and detailed report for any synthetic dataset vs. real world data.

Notebook	Launch	Description
Redact PII	Open in Colab	In this blueprint, we will create a transform policy to identify and redact or replace PII with fake values. We will then use the SDK to transform a dataset and examine the results.
Local Transform	Open in Colab	Label and transform sensitive data locally in your environment.
De-identify Data	Open in Colab	In this deep dive, we will walk through some of the more advanced features to de-identify data with the Transform API, including bucketing, date shifts, masking, and entity replacements.
Anonymize a Postgres Database	Open in Colab	This notebook walks through creating a policy using the Transform API to de-identify and anonymize data in a Postgres database for test use cases.