Gretel Evaluate generates a Synthetic Data Quality Score (SQS) report by comparing synthetic datasets to real-world data. There are no restrictions on evaluating synthetic data, regardless of its training source. The Evaluate API is accessible via CLI and SDK, and can be run in the Gretel Cloud or locally. As a fully managed service, it requires no additional infrastructure for setup or management.

Before training a machine learning model, you must set key hyperparameters like the number of layers, embedding dimensions, and training specifics. These choices significantly affect model quality, especially in generative models where optimized hyperparameters can mean the difference between low- and high-quality synthetic data. Gretel Tuner is a config-driven tool for efficiently tuning the hyperparameters of Gretel Synthetics models.

Gretel Workflows provide an easy way to automate and operationalize synthetic data into your existing AI/ML workflow. Gretel has integrations with cloud providers, data warehouses, and various ML tools and frameworks. Learn more about our connectors and integrations in the AI/ML tech stack in our docs.

Gretel jobs run within the Gretel Data Plane. Gretel provides two deployment options for the Gretel Data Plane that you may use depending on your requirements.

• Gretel Cloud: Gretel Cloud is a comprehensive, fully managed service for synthetic data generation and it operates within Gretel's cloud compute infrastructure, allowing Gretel to handle all concerns related to compute, automation, and scalability.
• Gretel Hybrid: Gretel Hybrid operates within your own cloud tenant and is deployed on Kubernetes. Gretel Hybrid is supported on GCP, Azure, and AWS through the use of the managed Kubernetes services offered by these cloud providers. Gretel Hybrid interfaces with the Gretel Control Plane API for job scheduling and job related metadata but customer owned data will never egress from your cloud environment. Gretel Hybrid is particularly well suited for handling sensitive or regulated data that cannot leave your cloud tenant's boundaries. Gretel Hybrid combines the benefits of using your infrastructure for training synthetic data models with Gretel’s advanced tools, offering a balance of control and convenience.