v1.0 Features:
- Three different decision analysis mechanisms to perform what-if analysis
- A simple "expert" rules engine to predict baseball hall of fame induction, contrasted with a Machine Intelligence solution
- Single and multiple machine learning models working together to predict baseball hall of fame ballot and induction probabilities
- Machine Learning models are surfaced via ML.NET in-memory for rapid inference (predictions)
- Surfaced via the Server-Side Blazor .NET Core web application framework using SignalR to deliver the predictions from the server to the web client at scale
- Self-contained application in a Docker container on DockerHub, allowing you to run it completely offline or locally
- Target development platform: .NET Core 3.x SDK
Live Demo Web Site: https://aka.ms/BaseballMLWorkbench
AI Architecture Details: https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/baseball-ml-workload
DockerHub Container Location: https://hub.docker.com/r/bartczernicki/baseballmachinelearningworkbench
Live Demo (Docker container hosted on Azure Container Instances): http://baseballmachinelearningworkbench.eastus2.azurecontainer.io
Full Get Started Guide: https://github.com/bartczernicki/MachineLearning-BaseballPrediction-BlazorApp/blob/master/GETSTARTED.md
