Overview - Future AGI

Dataset is the backbone of all data-driven workflows in the Future AGI. This section will help you to populate, structure and enrich the datasets, giving you the full control over how your data is created and managed. This will further allow you to focus on the downstream tasks, such as evaluations, experimentation and optimizations. Future AGI supports various methods of creating datasets including a synthetic data generator that will help you to create diverse datasets for your various unique use cases. This section covers:

Concepts: Foundational knowledge about dataset structure, including static and dynamic columns.
How-To Guides: Step-by-step instructions for dataset building and management, including:
- Creating synthetic data to generate diverse training examples.
- Changing column types to adapt to different data requirements.
- Creating static columns for fixed value data entries.
- Creating dynamic columns for automated data processing and transformation.
- Adding data using the SDK for seamless integration.
- Uploading datasets (JSON, CSV) for structured data ingestion.
- Manually creating datasets for custom data structuring.
- Importing datasets from Hugging Face to leverage pre-existing models.
- Adding data from existing datasets/experiments for iterative improvements.

By mastering dataset building, you can create structured, flexible datasets that serve as the foundation for AI-driven applications and workflows, optimize model performance, and automate data workflows.