YData helps data scientists unlock data's full potential. YData Fabric empowers users to easily understand and manage data assets, synthetic data for fast data access and pipelines for iterative and scalable flows. Automate data profiling for a simple and fast exploratory data analysis. Generate synthetic data that mimics the statistical properties and behaviour of the real data. Refine and improve processes with pipelines - consume the data, clean it, transform your data and work its quality to boost AI models' performance.
• synthetic data generation
• data profiling
• self-hosted deployment
• pipelines
• data catalog
• kubernetes-native solution
• fabric sdk
YData has an automated quality and privacy control process for every dataset generated with the goal to control the quality, utility, and privacy of the newly generated data.
YData has a strong foundation in the literature that supports the solution in regard to privacy safety.
YData's platform is deployed on your infrastructure (either cloud or on-premises), ensuring that there's never a data transfer between your company and YData.
Yes. It is not proprietary and does not contain PII (Personally identifiable information), plus YData generates an automated and detailed report regarding the quality of the generated data.
You can't. If you want to perform operational activities, business as usual, and single record operations, you'll need real data.
Data synthesization is the process of generating new data, not transforming the existing one. However, before YData generates new data, there's a preprocess of the original data in order to create new data with higher quality.
This is a very controversial question because bias can happen at several different levels, meaning it can happen at the level of the data collection, the data processing, or during the process of building a model.
SMOTE and ADASYN are very popular and familiar algorithms to be used by data scientists to deal with highly imbalanced datasets. While they have proven to be highly beneficial for low-dimensional data, the same does not apply.
We take care of all the data preprocessing that might not be dependent on business insights. We cover the following steps with our data preprocessing: encoding, inconsistencies detection, missing values imputation, and normalization while ensuring scalability.
The metrics vary depending on the type of data that we will be working with. As we support both tabular data with no time dependence and time-series data we’ve created a distinct validation flow for each type of data.
We also use different methods to be able to measure the level of privacy ensured by the newly generated data, from simple methods of Neighbouring Observation Removal to Data Source Prediction and Membership inference attacks.
There are many forms of Bias and Fairness in the data science lifecycle. The way YData helps organizations is by providing an easy way for the data science teams to understand the data they’re working with through an automated data exploratory analysis and then they can leverage the data synthesization tool to balance those datasets.
• 20+ connectors
• Data Catalog
• Automated Data Profiling
• Labs
• Synthetic Data Generation
• Fabric SDK
• Everything from Community plus:
• Automated Database Profiling
• Labs enhanced
• Synthetic Database Generation
• Pipelines
• Unlimited scalability
• Unlimited concurrent users
• Self-hosted on AWS & Azure
• Everything from Pay-as-you-go plus:
• Predictable pricing
• Deploy on private cloud
• Deploy on-premises
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