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Examples

Use case based tutorials.

Blueprint Notebooks

Synthetics

Notebook
Description
Description
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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.
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Walk through the basics of using Gretel's Python SDK to create a synthetic dataset from a Pandas DataFrame or CSV.
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Train a synthetic model locally and generate data in your environment.
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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.
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Balance demographic representation bias in a healthcare set using conditional data generation with a synthetic model.
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Create synthetic time-series data from a Pandas DataFrame or CSV.
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Use a synthetic model to boost the representation of an extreme minority class in a dataset by incorporating features from nearest neighbors.
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Use Gretel APIs to anonymize, synthesize, and then compare synthetic accuracy for a time-series dataset vs real world data.
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Run a sweep to automate hyper parameter optimization for a synthetic model using Weights and Biases.
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Augment a popular machine learning dataset with synthetic data to improve downstream accuracy and algorithmic fairness.
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Measure the effects of different differential privacy settings on a model's ability to memorize and replay secrets in a dataset.
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This notebook shows how to generate synthetic data directly from a multi-table relational database to support data augmentation and subsetting use cases.
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Generate realistic but synthetic text examples using an open-source implementation of the GPT-3 architecture.
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Generate synthetic daily oil price data using the DoppelGANger GAN for time-series data.
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Produce a quality score and detailed report for any synthetic dataset vs. real world data.

Transforms

Notebook
Launch
Description
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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.
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Label and transform sensitive data locally in your environment.
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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.
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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
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.
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Label managed and custom data types locally in your environment.
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In this blueprint, we analyze and label a set of freetext email dumps looking for PII and other potentially sensitive information using NLP.