Deeploy for your needs
The AI governance platform for real time control on both Generative and Predictive AI operations.
AI governance officer
Select or define control frameworks
Select from global, European, or company-specific frameworks to guide AI implementation. Control frameworks define requirements for successful implementation of AI use cases.
Assess use case risk levels
Determine risk levels through standardized assessments. Risk scores automatically determine which controls apply to each use case.
Configure approval workflows
Define which roles must approve deployments in each workspace.
Schedule periodic reviews
Set up automated reminders for recurring compliance reviews across all use cases in your workspace.
Assess applicable controls through AI lifecycle
Real-time technical checks validate control requirements throughout the AI lifecycle
AI engineer
Onboard any model as a deployment, from GenAI to Predictive AI
Choose how you want to onboard models: hosted on the Deeploy platform as managed deployment, integrated as external APIs, or just register the model.
Deploy versioned model artefacts from your preferred integration
Choose to deploy model artefact from Git based version control systems, MLFlow, Databricks, Huggingface or Azure model registry
Deploy to your preferred deployment service
Deeploy can manage deployments in Kubernetes (Kserve, default), AzureML and Sagemaker.
Choose from many available model frameworks
For managed deployments Deeploy provides numerous pre-built images, like Scikit-learn, XGBoost, LightGBM and Huggingface.
Configure out-of-the-box model explainability
Built-in support for model explanations like feature attribution to make model predictions interpretable even for non-technical users.
Automated gateway for monitoring, guardrailing, alerting, logging, authentication and load balancing
Deeploy creates an API gateway that handles load balancing, guardrailing, alerting and logging and authentication of your inferences out of the box.