FlutterFlow and Azure Machine Learning integration: Step-by-Step Guide 2024

   

This guide provides a detailed walkthrough on how to integrate FlutterFlow with Azure Machine Learning. Follow these steps carefully to set up a seamless connection between your FlutterFlow app and Azure ML.

 

Before starting, ensure you have the following:

• A FlutterFlow project
• An Azure account
• Basic knowledge of Dart and Flutter
• Access to Azure Machine Learning Studio

 

• Login to Azure Portal: Sign in to your Azure account at Azure Portal.
• Create a Machine Learning Workspace:
• Navigate to the "Machine Learning" service.
• Click on "Create workspace".
• Provide the necessary details like Workspace name, Resource group, Location, etc.
• Click "Review + create" to finalize the setup.
• Configure Workspace: Once the workspace is created, go to the workspace and start setting up your machine learning environment. This involves creating datasets, experiments, pipelines, and models.

 

• Develop Your Model:
• Use Azure ML Studio to create and train your machine learning model.
• Save and register the model in Azure ML.
• Deploy Your Model as a Web Service:
• Go to the "Endpoints" section in your Machine Learning workspace.
• Select "Deploy" and choose "Real-time endpoint".
• Configure the endpoint settings, such as compute type and scaling.
• Test the endpoint to ensure it’s working correctly. You can use the "Test" tab in the Azure portal to send sample data and check the responses.
• Copy Endpoint URL and Keys:
• Copy the REST endpoint URL for the deployed model.
• Note down the API key provided for authentication.

 

• Create a New Project:
• Login to FlutterFlow and create a new project.
• Choose the template that best suits your requirements.
• Design Your App:
• Use the FlutterFlow UI to design the screens of your application.
• Customize widgets, add navigation, and set initial properties as per your application's needs.

 

• Add Dependency:
• Open your pubspec.yaml file in FlutterFlow and add http package:

    
dependencies:
  flutter:
    sdk: flutter
  http: ^0.13.3
    
  

• Save to get the dependencies.

 

• Import Packages:
• Import the http package in your Dart file:

    
import 'package:http/http.dart' as http;
import 'dart:convert';
    
  

• Create Function to Call API:
• Write a function that makes a POST request to the Azure ML endpoint. Make sure to handle the API key and payload properly:

    
Future<Map<String, dynamic>> fetchPrediction(List<double> features) async {
  final url = 'YOUR_AZURE_ENDPOINT\_URL';
  final apiKey = 'YOUR_API_KEY';

  final headers = {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer $apiKey'
  };

  final body = jsonEncode({
    'input\_data': [
      {'data': features}
    ]
  });

  final response = await http.post(
    Uri.parse(url),
    headers: headers,
    body: body
  );

  if (response.statusCode == 200) {
    return jsonDecode(response.body);
  } else {
    throw Exception('Failed to get prediction');
  }
}
    
  

 

• Use API Call in Your App:
• Connect the function to your UI (e.g., on button press):

    
ElevatedButton(
  onPressed: () async {
    try {
      final features = [1.0, 2.0, 3.0]; // Sample data
      final prediction = await fetchPrediction(features);
      print('Prediction: $prediction');
      // Update your UI with the prediction result
    } catch (e) {
      print('Error: $e');
    }
  },
  child: Text('Get Prediction'),
);
    
  

 

• Run Your App:
• Ensure there are no errors in your code.
• Test the app and ensure it successfully calls the Azure ML API and displays the prediction results.
• Debug:
• Use Flutter's debugging tools to resolve any issues.
• Check network requests and responses to ensure data is being sent and received correctly.

 

By following these steps, you will have successfully integrated FlutterFlow with Azure Machine Learning. This enables you to leverage powerful machine learning models directly within your Flutter app, enriching the functionality and user experience.