YData Fabric* helps data science teams to collaborate and build the best training datasets and exponentially accelerate AI & ML while preserving the Security, Privacy & Fidelity of the data and without ever leaving your Azure cloud premises.

ONE CLICK deploy to Microsoft Azure. Highly scalable and available, connects to every database, warehouse or lake.

No need for customization or implementation project | Integrates with other MLOps platforms | Adapts to every organization authentication | Easy management of projects and teams
*Trusted by several Fortune 500 institutions, from North America & Europe.

Key Benefits
Improve ROI on AI

Microsoft Azure can save you money on in-house servers, and increase productivity while having flexibility for all your AI projects.

With YData on top of Azure and its capabilities to fix data issues, organizations can improve the overall quality in a purely data-centric way, While leveraging synthetic data generation capabilities for a multitude of use cases

Azure + YData allows you to build the best training datasets for your use cases and beat competition by unlocking the value of your most important asset - your data.

Scale your business quickly and flexibly

With YData Fabric on Microsoft Azure you can increase or decrease the amount of resources needed. Data Scientists may start on a small dataset to experiment, but it can quickly scale to production workloads and you need flexibility and availability.

Breaking data silos

In the age of GDPR and other compliance regulations, it has become even more difficult for companies to access and share data between different internal departments, geographies and especially external partners. YData together with Azure allows breaking the data silos and giving data-driven organizations means to access, share and better understand data using Synthetic Data Generation whilst guaranteeing both Data Privacy and Data Utility aspects.

Key Features

1. Data quality profiling helps data scientist better understand the existing data and what needs to be fixed in the dataset;
2. Embedded IDEs (Jupyter, VS Code, and more) make it easy and familiar for data scientists to make decisions upon the data preparation;
3. State-of-Art Synthetic data generation can be used to augment, balance, simulate or impute missing values in a dataset, depending on the needs for data quality improvement;
4. Pipelines allow users to constantly optimize the data preparation until a good result is achieved.

Use Cases Examples

FinanceAML & Fraud Detection

Credit Risk Scoring & Bias Mitigation