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:
- 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 6, Server-Side Blazor, ML.NET v1.5 - v1.7, 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