Maarten Van Segbroeck

Incorporating reasoning traces into synthetic datasets enhances AI transparency and trustworthiness.

Fine-tune CodeLlama with Gretel's Synthetic Text-to-SQL on BIRDBench, achieving a 36% relative improvement in EX and 38% in VES.

Train financial LLMs with Gretel's Synthetic Text-to-Python dataset to transform natural language into precise, domain-specific Python code for FinTech.

Gretel fine-tuned, synthetically-enhanced GLiNER models for better PII & PHI detection—datasets included.

Gretel's latest open synthetic dataset aims to enhance LLM interactions and contributes to the popular 'awesome-chatGPT-prompts' GitHub repository.

Leveraging Gretel Navigator to Create Diverse and Quality-Driven Question-Truth Pairs for RAG Evaluation

Use Gretel's synthetic data platform to replace, augment, or balance training datasets within MLOps pipelines like Vertex AI, Azure ML, and Amazon SageMaker.

Learn how to generate question-answer pairs from documents using a unique application built using Gretel Navigator.

Use Gretel DGAN and Gretel Tuner to generate time series data that accurately mirrors complex business rules and sequences

Create high-quality synthetic datasets of conversational dialogs safely tuned on your private, sensitive data with Gretel.

Generate private and shareable data automatically by triggering Gretel jobs in Amazon SageMaker