Gretel.ai and Carahsoft have partnered together to provide a series of self-guided tours of Gretel.ai's products and features. Similar to a live demo, the self-guided tours explores how Gretel.ai's products and features applies to a specific technology vertical such as Artificial Intelligence.
Learn about Gretel.ai's benefits, watch a short pre-recorded demo video, and download related resources. If interested in furthering the conversation, you can also schedule a live demo with a Gretel.ai expert from Carahsoft. Start a Self-Guided Tour now by selecting one below:
Gretel was created to help data scientists and developers test AI models without worry or security compromises through the usage of synthetic data. By developing artificial datasets with the same characteristics as an organization’s real data, developers can still create, develop, and test AI models that will work once launched into their organization’s live landscape. Available via AWS Marketplace, Google Cloud Marketplace, and Azure Marketplace, Gretel can start generating safe and accurate synthetic data and data models for your developers within minutes of downloading. Organizations can have peace of mind with Gretel as their synthetic data is provably private to mitigate GDPR, CCPA and HIPAA risks, and has led to zero fines from those safeguarding organizations.
Gretel Workflows are automated, end-to-end solutions for integrating synthetic data into your existing pipelines using scheduling, cloud storage, database, data warehouse connectors and no-code configurations. This allows synthetic data to be created on-demand and made accessible wherever and whenever you need it.
Gretel Workflows are automated, end-to-end solutions for integrating synthetic data into your existing pipelines using scheduling, cloud storage, database, data warehouse connectors and no-code configurations. This allows synthetic data to be created on-demand and made accessible wherever and whenever you need it.
Gretel Evaluate is an integral component of Gretel's toolset tailored for both generating and evaluating synthetic data. Its primary purpose revolves around gauging the effectiveness and usefulness of synthetic data derived from Gretel's generation methods. This solution offers capabilities to gauge the resemblance between synthetic data and the initial dataset. It assesses parameters such as statistical congruity, correlations, distributions and other traits. This assessment is vital for users in determining the applicability of synthetic data across different scenarios, including machine learning model training or analytical tasks, ensuring it retains essential attributes of the original data while safeguarding privacy.