Baseball Machine Learning Workbench is a web application that showcases performing decision analysis (decision thresholding, what-if analysis) using in-memory Machine Learning models with baseball data.

Live Demo Web Site: https://baseballmlworkbench.azurefd.net/
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
Full Get Started Guide: https://github.com/bartczernicki/MachineLearning-BaseballPrediction-BlazorApp/blob/master/GETSTARTED.md

The application has the following features:

• Historical position player (batters) up to the end of the 2023 season
• 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

Architecture - Cloud Deployment Diagram:

Project Structure (Verified):

• Visual Studio 2019 v4.0 for Windows/Mac - Visual Studio 2022, .NET Core 3.x - .NET 8, Server-Side Blazor, ML.NET v1.5 - v3.0.1, Azure SignalR (optional for massively scaling message communication for Azure deployments)
• Note: Updated Azure service versions or NuGet package references could work

More Information:

• ML.NET: https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet
• Blazor: https://dotnet.microsoft.com/apps/aspnet/web-apps/blazor
• Historical Baseball Statistics Database (used as the model training and inference data set): http://www.seanlahman.com/baseball-archive/statistics/
• How to Measure Anything (Amazon book link): https://www.amazon.com/How-Measure-Anything-Intangibles-Business-ebook/dp/B00INUYS2U/ref=sr_1_1?dchild=1&keywords=how+to+measure+anything&qid=1588713606&sr=8-1
• Decision Management Systems (Amazon book link): https://www.amazon.com/Decision-Management-Systems-Practical-Predictive/dp/0132884380