Power Generative AI with Synthetic Data
The data challenge
Large Language Models (LLMs) are trained extensively on the vast amount of publicly available data. Further extracting value from these models involves additional training on new or private data. This 'last mile' training presents AI teams with challenges related to data privacy, quality, and availability. These hurdles are common to both enterprises looking to adapt LLMs for domain-specific tasks as well as frontier AI teams building their own foundation models.
- Data Quality
Issues with data quality such as missing fields and unwanted bias greatly impact model performance, jeopardizing the utility of models in production.
- Data Availability
Training models requires large amounts of cleaned, curated and annotated data. Collecting ground-truth data is time-consuming and expensive.
- Data Privacy
Exposing sensitive datasets to public models is akin to placing them on the public cloud, risking improper access, memorization, or leakage.
Key Benefits
- Improve LLM performance
Multiple synthetic data models purpose-built for producing high-quality and fully labeled data for more robust LLMs.
- Faster time to value
Accelerate generative AI applications with on-demand access to training data that embeds directly in your LLM training workflows.
- Safe ML training
Mathematically guaranteed privacy and mitigated risks of regulatory fines with provably private synthetic data.
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- Join the Synthetic Data Community
Join our Discord to connect with the Gretel team and engage with our community.
- Read our docs
Set up your environment and connect to our SDK.