The Data-Centric AI toolkit for data quality profiling and synthetic data generation
We are proud to announce that the YData SDK is now officially available to the broader data science community. With a single line of code, any team or individual contributor is now able to go from raw data to high-quality data.
YData just launched the new ydata-sdk that enables any user or individual contributor to start improving the quality of their data assets. With the new code base interface, users are able to explore and improve their data in a seamless and flexible way, fully integrable into existing flows and platforms.
The SDK allows users to profile their datasets, investigate the generated data quality alerts, and use synthetic data to boost data augmentation, reduce data bias, and foster data sharing while mitigating privacy concerns.
This is a major accomplishment towards our mission to help data science teams access, understand, and improve their data to build accurate and reliable machine learning models with significant impacts on business outcomes.
Same functionality in a smaller shell
The YData SDK is a set of integrated components for data ingestion, standardized data quality evaluation, and data improvement that can easily be accessed through a Python Interface.
This is outstanding in terms of user experience, as users can now incorporate the SDK into any platform running Python and explore YData’s features across several use cases:
Improve model training with data augmentation
Take advantage of YData-SDK to overcome problems with the lack of data or underrepresented data. Data augmentation guarantees that machine learning models are trained with sufficient data to grasp the existing concepts in data, boosting classification performance.
Reduce bias with data balancing
Lack of concept representation is one of the main sources of data bias in real-world domains. With YData-SDK, the representation of disproportionate categories of data can be increased via data balancing, mitigating bias and fostering data fairness.
Foster data sharing and mitigate privacy concerns with synthetic data
With YData-SDK, data teams can take advantage of synthetic data as a privacy-enhancing technique. The synthesizers are optimized to hold real data value while enhancing the security of private or sensitive data, enabling data-sharing between or within organizations without risking data leakage.
Generative AI with one line of code
The YData SDK is available to the data science community and comes with detailed documentation to guide the users through the adoption of their new favorite data quality toolkit. It includes synthetic data generation for tabular, time-series, transactional, and multi-table datasets.
The SDK can be used by anyone and it only takes a few minutes to get started. It is installed through PyPI - users will be prompted to create a YData account to get the access token. This step-by-step will help quickstart the journey with ydata-sdk. After all is set, the SDK can be used on any platform from a simple Python script to a Jupyter Notebook or Google Colab!
Our team is excited to provide such a powerful and intuitive asset for data scientists to solve some of the most challenging problems in the field of AI: securely collaborate on sensitive data and improve machine learning models with smart data.
We welcome your feedback and suggestions as we continue to improve and evolve our SDK. If you have any questions, please do not hesitate to reach out to us on our Discord Server.
We’re excited to keep working alongside you to develop the best tools to improve the quality of your data!
