ScalableSentimentAnalysisBlazorWebApp

SENTIMENT ANALYSIS: Blazor sample (ASP.NET Core 3.0) optimized for scalability and performance when running/scoring an ML.NET model (Using the new '.NET Core Integration Package for ML.NET')

ML.NET version	Status	App Type	Data type	Scenario	ML Task	Algorithms
v1.4	Up-to-date	Blazor / ASP.NET Core 3.0	Single data sample	Sentiment Analysis	Binary Classification	Linear Classification

Goal

The goal is to be able to make SENTIMENT ANALYSIS prediction/detection of what the user is writing in a very UI interactive app (BLAZOR based) in the client side and running an ML.NET model (Sentiment analysis based on binary-classification) in the server side.

Here's a screenshot of the app while running:

From ML.NET perspective, the goal is to optimize the ML.NET model executions in the server by sharing the ML.NET objects used for predictions across Http requests and being able to implement very simple code to be used by the user when predicting, like the following line of code that you could write on any ASP.NET Core controller's method or custom service class:

SamplePrediction prediction = _predictionEnginePool.Predict(sampleData);

As simple as a single line. The object predictionEnginePool will be injected in the controller's constructor or into you custom class.

Internally, it is optimized so the object dependencies are cached and shared across Http requests with minimum overhead when creating those objects.

Architecture

Blazor allows you to run C# code in the client side, as shown in the architecture figure.

For this sample we chose to run the ML.NET model in the server side, so the model is protected within the service. However, for maximum perf and lower latency you could also run the ML.NET model in the browser (thanks to BLAZOR and WebAssembly) in a similar way you can run any model on a console .NET Core app.

Running the model in the server side allows you to protect the model plus the following benefits:

• The web app can run on a mobile device (ARM) since it is just running HTML and WebAssembly. If the ML.NET model runs in the browser, it wouldn't run on a regular mobile device because ARM is not currently supported by ML.NET.
• The 'WebAPI' service could be consumed by other remote apps

Problem

The problem running/scoring an ML.NET model in multi-threaded applications comes when you want to do single predictions with the PredictionEngine object and you want to cache that object (i.e. as Singleton) so it is being reused by multiple Http requests (therefore it would be accessed by multiple threads). That's is a problem because the Prediction Engine is not thread-safe (ML.NET issue, Nov 2018).

Solution

Use the new '.NET Core Integration Package' that implements Object Pooling of PredictionEngine objects for you

'.NET Core Integration Package' NuGet

Package name: Microsoft.Extensions.ML Package version: 0.15.1

Basically, with this component, you inject/use the PredictionEngine object pooling in a single line in your Startup.cs, like the following:

services.AddPredictionEnginePool<SampleObservation, SamplePrediction>()
        .FromFile(Configuration["MLModel:MLModelFilePath"]);

Then you just need to call the Predict() function from the injected PredictionEnginePool, like the following code you can implement on any controller:

//Predict sentiment
SamplePrediction prediction = _predictionEnginePool.Predict(sampleData);

It is that simple.

For a much more detailed explanation of a PredictionEngine Object Pool comparable to the implementation done in the new '.NET Core Integration Package', including design diagrams, read the following blog post:

Detailed Blog Post for further documentation:

How to optimize and run ML.NET models on scalable ASP.NET Core WebAPIs or web apps

NOTE: YOU DON'T NEED TO MAKE THE IMPLEMENTATION EXPLAINED IN THE BLOG POST. IT IS IMPLEMENTED FOR YOU IN THE '.NET INTEGRATION PACKAGE'.