titanicai.dev

Project Overview

Components

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 TitanicAI 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.

Folders

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