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Deploy a model in an ASP.NET Core Web API

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Learn how to serve a pre-trained ML.NET machine learning model on the web using an ASP.NET Core Web API. Serving a model over a web API enables predictions via standard HTTP methods.

Prerequisites

Create ASP.NET Core Web API project

  1. Start Visual Studio 2022 and select Create a new project.

  2. In the Create a new project dialog:

    • Enter Web API in the search box.
    • Select the ASP.NET Core Web API template and select Next.

  3. In the Configure your project dialog:

    • Name your project SentimentAnalysisWebAPI.
    • Select Next.

  4. In the Additional information dialog:

    • Uncheck Do not use top-level statements.
    • Select Create.

  5. Install the following NuGet packages:

    For more details on installing NuGet packages in Visual Studio, see the Install and use a NuGet package in Visual Studio guide.

Add model to ASP.NET Core Web API project

  1. Copy your pre-built model to your SentimentAnalysisWebAPI project directory.

  2. Configure your project to copy your model file to the output directory. In Solution Explorer:

    • Right-click the model zip file and select Properties.
    • Under Advanced, change the value of Copy to Output Directory to Copy if newer.

Create data models

You need to create some classes to define the schema of your model input and output.

Note

The properties of your input and output schema classes depend on the dataset columns used to train your model as well as the machine learning task (regression, classification, etc.).

In your Program.cs file:

  1. Add the following using statements:

    using Microsoft.ML.Data;
using Microsoft.Extensions.ML;

  2. At the bottom of the file, add the following classes:

    Model input

    For this model, the input contains a single property SentimentText which is a string that represents a user comment.

    public class ModelInput
{
    public string SentimentText;
}

    Model output

    Once the model evaluates the input, it outputs a prediction with three properties: Sentiment, Probability, and Score. In this case, the Sentiment is the predicted sentiment of the user comment and the Probability and Score are confidence measures for the prediction.

    public class ModelOutput
{
    [ColumnName("PredictedLabel")]
    public bool Sentiment { get; set; }

    public float Probability { get; set; }

    public float Score { get; set; }
}

Register PredictionEnginePool for use in the application

To make a single prediction, you have to create a PredictionEngine. PredictionEngine is not thread-safe. Additionally, you have to create an instance of it everywhere it is needed within your application. As your application grows, this process can become unmanageable. For improved performance and thread safety, use a combination of dependency injection and the PredictionEnginePool service, which creates an ObjectPool of PredictionEngine objects for use throughout your application.

The following link provides more information if you want to learn more about dependency injection in ASP.NET Core.

Add the following code to your Program.cs file:

builder.Services.AddPredictionEnginePool<ModelInput, ModelOutput>()
    .FromFile(modelName: "SentimentAnalysisModel", filePath: "sentiment_model.zip", watchForChanges: true);

At a high level, this code initializes the objects and services automatically for later use when requested by the application instead of having to manually do it.

Machine learning models are not static. As new training data becomes available, the model is retrained and redeployed. One way to get the latest version of the model into your application is to restart or redeploy your application. However, this introduces application downtime. The PredictionEnginePool service provides a mechanism to reload an updated model without restarting or redeploying your application.

Set the watchForChanges parameter to true, and the PredictionEnginePool starts a FileSystemWatcher that listens to the file system change notifications and raises events when there is a change to the file. This prompts the PredictionEnginePool to automatically reload the model.

The model is identified by the modelName parameter so that more than one model per application can be reloaded upon change.

Tip

Alternatively, you can use the FromUri method when working with models stored remotely. Rather than watching for file changed events, FromUri polls the remote location for changes. The polling interval defaults to 5 minutes. You can increase or decrease the polling interval based on your application's requirements. In the code sample below, the PredictionEnginePool polls the model stored at the specified URI every minute.

services.AddPredictionEnginePool<SentimentData, SentimentPrediction>()
  .FromUri(
      modelName: "SentimentAnalysisModel",
      uri:"https://github.com/dotnet/samples/raw/main/machine-learning/models/sentimentanalysis/sentiment_model.zip",
      period: TimeSpan.FromMinutes(1));

Map predict endpoint

To process your incoming HTTP requests, create an endpoint.

Replace the / endpoint with the following:

var predictionHandler =
    async (PredictionEnginePool<ModelInput, ModelOutput> predictionEnginePool, ModelInput input) =>
        await Task.FromResult(predictionEnginePool.Predict(modelName: "SentimentAnalysisModel", input));

app.MapPost("/predict", predictionHandler);

The /predict endpoint accepts HTTP POST requests and uses the prediction engine pool to return a prediction using the provided input.

When finished, your Program.cs should look like the following:

using Microsoft.ML.Data;
using Microsoft.Extensions.ML;

var builder = WebApplication.CreateBuilder(args);

builder.Services.AddPredictionEnginePool<ModelInput, ModelOutput>()
    .FromFile(modelName: "SentimentAnalysisModel", filePath: "sentiment_model.zip", watchForChanges: true);

var app = builder.Build();

var predictionHandler =
    async (PredictionEnginePool<ModelInput, ModelOutput> predictionEnginePool, ModelInput input) =>
        await Task.FromResult(predictionEnginePool.Predict(modelName: "SentimentAnalysisModel", input));

app.MapPost("/predict", predictionHandler);

app.Run();

public class ModelInput
{
    public string SentimentText;
}

public class ModelOutput
{
    [ColumnName("PredictedLabel")]
    public bool Sentiment { get; set; }

    public float Probability { get; set; }

    public float Score { get; set; }
}

Test web API locally

Once everything is set up, it's time to test the application.

  1. Run the application.

  2. Open PowerShell and enter the following code where PORT is the port your application is listening on.

    Invoke-RestMethod "https://localhost:<PORT>/predict" -Method Post -Body (@{SentimentText="This was a very bad steak"} | ConvertTo-Json) -ContentType "application/json"

    If successful, the output should look similar to the text below:

    sentiment probability score
--------- ----------- -----
False         0.5     0

Congratulations! You have successfully served your model to make predictions over the internet using an ASP.NET Core Web API.

Next Steps