Serverless Examples

Serverless allows users to build and run applications without managing the underlying infrastructure. This model is ideal for projects where managing server capacity and scaling concerns are handled by Ori. It's especially suitable for use cases that have variable workloads, such as:

• Machine learning and AI: Training models or running inference based on dynamic data sets can be resource-intensive at irregular intervals, which fits well with the serverless model of on-demand resource allocation.
• Event-driven applications: These respond to real-time events such as user actions, sensor outputs, or messages from other applications, making serverless ideal due to its ability to scale automatically.
• Batch processing: Jobs that need to process large volumes of data in batches benefit from serverless because resources can be allocated dynamically to handle the load and then released.

Follow the link to Stable Diffusion for a running example.