| Component | Description |
|---|---|
| Model | Model trained and built with R Code using R Studio. You can prepare the data, wrangle it, modify, visualise and run stats. There are many algorithms to choose from and many ways to parameterise your AI training process. You can also set criteria which model to choose for you final published API as well as ways to test accuracy of you chosen model. |
| Api | HTTP endpoint for your trained AI model. This project gives you an easy way to package your trained model (RDS file) and expose it via HTTP endpoint so that all you need to do from your client applications is to make an API call to the endpoint. |
| Webapp | Front end webapp for the ai-titanic R Model. To accompany this project, you can run an interactive website where you can pass different inputs (gender, class, siblings, etc.) to the AI model and get your Titanic survival outcome back. |
| Folder | Description |
|---|---|
| .cicd | ado: Azure DevOps Build Yaml aks: AKS Deployment compose: Docker Compose Yaml docker: Dockerfiles eks: EKS Deployment gke: GKE Deployment helm: Helm Chart Yaml jekyll: Jekyll Files kubernetes: Kubernetes Yaml |
| docs | Solution documentation files, refer to Documentation section on the home page. |
| run | run-api.sh: run Api locally run-rstudio.sh: run R Studio locally run-webapp.sh: run Webapp locally |
| src | model: R Code source code webapp: ASPNET C# Web application source code |