AI
Test Data Generation: Uses, Benefits, and Tips
Test data generation is the process of creating new data that replicates an original dataset. Here’s how developers and data engineers use it.
Read more...Generate Synthetic Databases with Gretel Relational
Introducing Gretel Relational, enabling organizations to generate high-quality synthetic databases while preserving cross-table relationships.
Read more...Teaching AI to Think: A New Approach with Synthetic Data and Reflection
Gretel's synthetic GSM8k dataset shows an 84% improvement for AI Reasoning tasks vs synthetic data generated without reflection.
Read more...Gretel’s New Data Privacy Score
Gretel releases industry standard synthetic tabular data privacy evaluation and risk-based scoring system.
Read more...AWS + Gretel Synthetic Data Accelerator Program for Generative AI
How our new Synthetic Data Accelerator Program with AWS will help enterprises scale responsible AI systems fast.
Read more...Introducing Gretel MLOps
Use Gretel's synthetic data platform to replace, augment, or balance training datasets within MLOps pipelines like Vertex AI, Azure ML, and Amazon SageMaker.
Read more...Generate Question-Truth Pairs from Documents with Gretel Navigator
Learn how to generate question-answer pairs from documents using a unique application built using Gretel Navigator.
Read more...Synthetic Time Series Data Creation for Finance
How we generated high-quality synthetic time-series data for one of the largest financial institutions in the world.
Read more...Conditional Text Generation by Fine Tuning Gretel GPT
Augment machine learning datasets with synthetically generated text and labels using an open-source implementation of GPT-3.
Read more...Deep dive on generating synthetic data for Healthcare
Take a deep dive on training Gretel’s open-source, synthetic data library to generate electronic health records that protect individual privacy (PII).
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