Under the Kubernetes environment, setting up and managing each endpoint for backend TrainDB-ML microservice (model training, inferencing etc.), that clients can consume can be challenging.
Problem
When the TrainDB-ML API changes, the client must change as well.
Forces
The number of service instances and their locations (host+port) changes dynamically
Partitioning into services can change over time and should be hidden from clients including TrainDB-main.
Solution
The solution for the above problem is the Gateway Routing pattern.
Gateway Routing pattern is Route requests to multiple microservices (ML training, inference, model metadata querying, etc.) with exposing a single endpoint.
feat: [Task] TrainDB-ML front-end interface implementation
Context
Problem
Forces
Solution
References
See also: #5