A flexible data ingestion library for various file formats
Project description
Data Ingestors
📄 Description
A robust data ingestion framework for machine learning pipelines. This repository provides tools and utilities for managing, processing, and validating training/test datasets. It supports various data sources, formats, and processing pipelines, making it easier to create and maintain ML datasets.
🛠️ Tech Stack
- Python 3.x
- Docker (for containerization)
- Data processing libraries (Pandas, NumPy)
🚀 Installation & Usage Instructions
- Clone the repository
- Install dependencies:
pip install -r src/requirements.txt
- Configure your environment
- Follow the documentation guide to Create Your Training/Test Dataset
📦 Features
- Multi-source data ingestion
- Data validation and preprocessing
- Database integration
- API endpoints for data management
- Containerized deployment
- Kubernetes support
📜 License
This project is licensed under the MIT License - see the LICENSE file for details.
📞 Support
For additional support or questions, please refer to our documentation or contact the Tracebloc support team at support@tracebloc.io.
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