Deploy and monitor models into a production environment to create real-world, data-driven solutions. Make them user-ready.
- Schedule your models in real-time or desired intervals. Your time, your choice.
- Monitor feature and label drifts every step along the way, don’t wait until something goes wrong.
- Understand how models function by using our explainability module. Detect bias and ensure fairness.
Deploy Module where the productization phase is developed. The productization phase in the Octai is essential for turning the developed artificial intelligence solution into a standalone application and providing access to third-party users and applications. This phase involves creating and managing deployments that enable AI models to be utilized in real-world scenarios.
Here's a more detailed breakdown of this phase:
Deployment module: This module is designed to package and run the AI solution as a standalone application, providing a user-friendly interface and the necessary tools to create endpoints for third-party users and applications. This module ensures that the AI model is ready for integration with external systems and can be easily consumed by clients.
Managing deployments: In the deployment module, you can review previously created deployments, monitor their performance, and manage their configurations. New deployments can be easily created by clicking the "New Deployment" button, guiding you through the process of deploying the trained model.
API endpoints: Once the model is deployed, the platform generates API endpoints, which serve as access points for third-party applications to interact with the model. These endpoints facilitate the exchange of data between the model and external systems, allowing users to send input data for processing and receive predictions or classifications in return.
Single deployment: In a single deployment, each feature in the model can be evaluated individually and executed in real-time. This type of deployment is suitable for applications that require on-the-fly predictions or classifications, such as recommendation systems, chatbots, or image recognition systems.
Batch deployment: With a batch deployment, the model processes a predetermined dataset in bulk, generating predictions or classifications for the entire dataset at once. This type of deployment is ideal for use cases where the model needs to process large volumes of data, such as forecasting, fraud detection, or customer segmentation.
By carefully designing and managing the productization phase, you can ensure that the AI solution is easily accessible, scalable, and ready for integration with external systems. This enables third-party users and applications to leverage the power of the AI model and apply its capabilities to a wide range of real-world scenarios.
Updated 5 months ago