Serve Model Azure Functions How-To

docs/machine-learning/how-to-guides/serve-model-serverless-azure-functions-ml-net.md

@@ -1,28 +1,28 @@
 ---
-title: Deploy ML.NET Model to Azure Functions
+title: "How-To: Deploy ML.NET machine learning model to Azure Functions"
 description: Serve ML.NET sentiment analysis machine learning model for prediction over the internet using Azure Functions
-ms.date: 03/08/2019
-ms.custom: mvc,how-to
+ms.date: 04/29/2019
+author: luisquintanilla
+ms.author: luquinta
+ms.custom: mvc, how-to
 #Customer intent: As a developer, I want to use my ML.NET Machine Learning model to make predictions through the internet using Azure Functions
 ---
 
-# How-To: Use ML.NET Model in Azure Functions
+# How-To: Deploy ML.NET machine learning model to Azure Functions
 
-This how-to shows how individual predictions can be made using a pre-built ML.NET machine learning model through the internet in a serverless environment such as Azure Functions.
-
-> [!NOTE]
-> This topic refers to ML.NET, which is currently in Preview, and material may be subject to change. For more information, visit [the ML.NET introduction](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet).
-
-This how-to and related sample are currently using **ML.NET version 0.10**. For more information, see the release notes at the [dotnet/machinelearning github repo](https://github.com/dotnet/machinelearning/tree/master/docs/release-notes).
+Learn how to deploy a pre-trained ML.NET machine learning model for predictions over HTTP through an Azure Functions serverless environment.
 
 ## Prerequisites
 
 - [Visual Studio 2017 15.6 or later](https://visualstudio.microsoft.com/downloads/?utm_medium=microsoft&utm_source=docs.microsoft.com&utm_campaign=inline+link&utm_content=download+vs2017) with the ".NET Core cross-platform development" workload and "Azure development" installed.
 - [Azure Functions Tools](/azure/azure-functions/functions-develop-vs#check-your-tools-version)
 - Powershell
-- Pre-trained model.
-  - Use the [ML.NET Sentiment Analysis tutorial](../tutorials/sentiment-analysis.md) to build your own model.
-  - Download this [pre-trained sentiment analysis machine learning model](https://github.com/dotnet/samples/blob/master/machine-learning/models/sentimentanalysis/sentiment_model.zip)
+- Pre-trained model
+    - Use the [ML.NET Sentiment Analysis tutorial](../tutorials/sentiment-analysis.md) to build your own model.
+
+    or 
+
+    - Download this [pre-trained sentiment analysis machine learning model](https://github.com/dotnet/samples/blob/master/machine-learning/models/sentimentanalysis/sentiment_model.zip)
 
 ## Create Azure Functions Project
 
@@ -53,23 +53,21 @@ Create a class to predict sentiment. Add a new class to your project:
 
     The *AnalyzeSentiment.cs* file opens in the code editor. Add the following `using` statement to the top of *GitHubIssueData.cs*:
 
-```csharp
-using System;
-using System.IO;
-using System.Threading.Tasks;
-using Microsoft.AspNetCore.Mvc;
-using Microsoft.Azure.WebJobs;
-using Microsoft.Azure.WebJobs.Extensions.Http;
-using Microsoft.AspNetCore.Http;
-using Microsoft.Extensions.Logging;
-using Newtonsoft.Json;
-using Microsoft.ML;
-using Microsoft.ML.Core.Data;
-using Microsoft.ML.Data;
-using MLNETServerless.DataModels;
-```
+    ```csharp
+    using System;
+    using System.IO;
+    using System.Threading.Tasks;
+    using Microsoft.AspNetCore.Mvc;
+    using Microsoft.Azure.WebJobs;
+    using Microsoft.Azure.WebJobs.Extensions.Http;
+    using Microsoft.AspNetCore.Http;
+    using Microsoft.Extensions.Logging;
+    using Newtonsoft.Json;
+    using Microsoft.ML;
+    using SentimentAnalysisFunctionsApp.DataModels;
+    ```
 
-### Create Data Models
+## Create Data Models
 
 You need to create some classes for your input data and predictions. Add a new class to your project:
 
@@ -82,8 +80,8 @@ You need to create some classes for your input data and predictions. Add a new c
     using Microsoft.ML.Data;
     ```
 
-    Remove the existing class definition and add the following code to the SentimentData.cs file:
-
+    Remove the existing class definition and add the following code to the *SentimentData.cs* file:
+    
     ```csharp
     public class SentimentData
     {
@@ -104,42 +102,65 @@ You need to create some classes for your input data and predictions. Add a new c
     Remove the existing class definition and add the following code to the *SentimentPrediction.cs* file:
 
     ```csharp
-    public class SentimentPrediction
+    public class SentimentPrediction : SentimentData
     {
         [ColumnName("PredictedLabel")]
         public bool Prediction { get; set; }
     }
     ```
 
-### Add Prediction Logic
+`SentimentPrediction` inherits from `SentimentData` which provides access to the original data in the `Text` property as well as the output generated by the model.
+
+## Load the model into the function
+
+Insert the following code inside the *AnalyzeSentiment* class:
+
+```csharp
+// Define MLContext
+static MLContext _mlContext;
+
+// Define model
+static ITransformer _model;
+
+// Define model's DataViewSchema
+static DataViewSchema _modelSchema;
+
+// Define PredictionEngine
+static PredictionEngine<SentimentData, SentimentPrediction> _predictionEngine;
+
+// AnalyzeSentiment class constructor
+static AnalyzeSentiment()
+{
+    // Create MLContext
+    _mlContext = new MLContext();
+
+    // Load Model
+    _model = _mlContext.Model.Load("MLModels/sentiment_model.zip", out _modelSchema);
+
+    // Create Prediction Engine
+    _predictionEngine = _mlContext.Model.CreatePredictionEngine<SentimentData, SentimentPrediction>(_model);
+}
+```
+
+The constructor contains initialization logic for the [`MLContext`](xref:Microsoft.ML.MLContext), model and [`PredictionEngine`](xref:Microsoft.ML.PredictionEngine%602) so that it can be shared throughout the lifecycle of the function instance. This approach reduces the need to load the model from disk each time the `Run` method executes. 
+
+## Use the model to make predictions
 
 Replace the existing implementation of *Run* method in *AnalyzeSentiment* class with the following code:
 
 ```csharp
 public static async Task<IActionResult> Run(
-    [HttpTrigger(AuthorizationLevel.Function,"post", Route = null)] HttpRequest req,
-    ILogger log)
+[HttpTrigger(AuthorizationLevel.Function, "post", Route = null)] HttpRequest req,
+ILogger log)
 {
     log.LogInformation("C# HTTP trigger function processed a request.");
 
-    //Create Context
-    MLContext mlContext = new MLContext();
-
-    //Load Model
-    using (var fs = File.OpenRead("MLModels/sentiment_model.zip"))
-    {
-        model = mlContext.Model.Load(fs);
-    }
-
     //Parse HTTP Request Body
     string requestBody = await new StreamReader(req.Body).ReadToEndAsync();
     SentimentData data = JsonConvert.DeserializeObject<SentimentData>(requestBody);
-
-    //Create Prediction Engine
-    PredictionEngine<SentimentData, SentimentPrediction> predictionEngine = model.CreatePredictionEngine<SentimentData, SentimentPrediction>(mlContext);
-
+
     //Make Prediction
-    SentimentPrediction prediction = predictionEngine.Predict(data);
+    SentimentPrediction prediction = _predictionEngine.Predict(data);
 
     //Convert prediction to string
     string isToxic = Convert.ToBoolean(prediction.Prediction) ? "Toxic" : "Not Toxic";
@@ -149,22 +170,24 @@ public static async Task<IActionResult> Run(
 }
 ```
 
+When the `Run` method executes, the incoming data from the HTTP request is deserialized and used as input for the [`PredictionEngine`](xref:Microsoft.ML.PredictionEngine%602). The [`Predict`](xref:Microsoft.ML.PredictionEngineBase%602.Predict*) method is then called to generate a prediction and return the result to the user. 
+
 ## Test Locally
 
 Now that everything is set up, it's time to test the application:
 
 1. Run the application
 1. Open PowerShell and enter the code into the prompt where PORT is the port your application is running on. Typically the port is 7071.
 
-```powershell
-Invoke-RestMethod "http://localhost:<PORT>/api/AnalyzeSentiment" -Method Post -Body (@{Text="This is a very rude movie"} | ConvertTo-Json) -ContentType "application/json"
-```
-
-If successful, the output should look similar to the text below:
+    ```powershell
+    Invoke-RestMethod "http://localhost:<PORT>/api/AnalyzeSentiment" -Method Post -Body (@{Text="This is a very rude movie"} | ConvertTo-Json) -ContentType "application/json"
+    ```
 
-```powershell
-Toxic
-```
+    If successful, the output should look similar to the text below:
+
+        ```powershell
+        Toxic
+        ```
 
 Congratulations! You have successfully served your model to make predictions over the internet using an Azure Function.