Access to quality training data is one of the biggest obstacles to building with generative AI. Gretel Navigator opens the door to generating high-quality diverse synthetic data quickly and on-demand. This allows teams to innovate faster, shorten time in bringing ML solutions to production, and to substantially lower the costs of AI development.
In this workshop, we will discuss how Gretel Navigator was used to generate the recently released synthetic Text-to-SQL dataset. This dataset was published under an open-source license to address the need for high-quality data. It quickly became the #1 trending dataset on Hugging Face, boasting 200+ likes and 1k+ downloads in one week and reinforcing the need for high-quality, easily-accessible data in the market. After discussing why Gretel Navigator was instrumental in generating this dataset, we will also use the dataset to fine-tune a small language model (SLM) and benchmark its performance against other LLMs on SQL tasks.
Join us to learn how to:
- Generate synthetic training data with Gretel Navigator
- Employ contextual tags to design synthetic data for your specific needs
- Fine-tune an SLM
- Speed-up innovation and reduce AI development costs
