How To Integrate Machine Learning In Android Apps in 7 Simple Steps?

How to integrate Machine learning in Android apps? If you are also one of those who have been just thinking of applying ML technology to Android apps, then this blog is for you.

But before you get into the tutorial to add ML into the Android app, just take a moment to understand “ Why Machine Learning Is Catching the Hype Among Businesses?”…

According to Statista’s research, the global revenue generated from machine learning is expected to witness significant growth in the coming years. 

In 2020, the revenue from ML technologies and services reached approximately $28.5 billion worldwide. By 2025, it is projected to exceed $96 billion, representing a substantial increase in market size and revenue generation.

These days businesses are increasingly turning to machine learning (ML) approaches to manage their operations and drive success in the highly competitive digital landscape. 

Since ML offers businesses the ability to harness the power of data and extract valuable insights, enabling them to make data-driven decisions and optimize processes. 

By integrating ML into their Android apps, businesses can leverage advanced algorithms and predictive analytics to gain a competitive edge. 

ML enables personalized experiences for customers, leading to higher customer satisfaction and retention rates. 

Moreover, ML allows businesses to automate routine tasks, improve operational efficiency, and streamline workflows. So all in all with ML-powered Android apps, businesses can unlock new opportunities, make more informed decisions, and stay at the forefront of innovation in today’s data-centric business environment.

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But the central question is what industries can integrate machine learning in Android apps? Do you need to hire a software developer in India to integrate it or is it simple to apply on your own? 

Well, that tutorial will help you understand how simple and complex it is to implement machine learning in Android apps. But first, we will check what industries can apply ML in Android apps.

What Industries Can Integrate Machine Learning in Android Apps?

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Machine learning integration in Android apps is not limited to specific industries. In fact, it has the potential to transform and enhance operations across various sectors, giving businesses a competitive edge in today’s digital landscape. But all you need is to hire a mobile app developer in India to integrate ML into Android. Here are some industries that can greatly benefit from integrating machine learning into their Android apps:

• E-commerce: Machine learning can be utilized to personalize product recommendations, improve search results, analyze customer behavior, detect fraudulent activities, and optimize pricing strategies. This helps businesses offer a tailored and engaging shopping experience while increasing conversions and customer satisfaction.

• Healthcare: Machine learning in healthcare apps can assist in disease diagnosis, medical imaging analysis, patient monitoring, drug discovery, and personalized treatment plans. 

By leveraging ML algorithms, healthcare providers can improve patient outcomes, enhance diagnostics, and streamline administrative processes.

• Financial Services: Machine learning enables intelligent fraud detection, risk assessment, credit scoring, algorithmic trading, and chatbot-based customer support in the financial sector. 

By integrating ML into Android apps, financial institutions can enhance security, automate processes, deliver personalized financial advice, and improve customer experiences.

• Media and Entertainment: ML integration in Android apps allows for content recommendation, sentiment analysis, video and audio recognition, personalized playlists, and targeted advertising. 

This enables media companies to provide customized experiences, optimize content delivery, and engage users on a deeper level.

• Education: Machine learning can be applied in educational apps for personalized learning paths, intelligent tutoring, automated grading, adaptive assessments, and content recommendation based on individual learning styles. 

ML integration enhances educational experiences, boosts engagement, and helps learners achieve their goals more effectively.

• Transportation and Logistics: ML-powered Android apps can optimize route planning, demand forecasting, vehicle maintenance, fleet management, and real-time logistics tracking. This leads to cost savings, improved efficiency, and enhanced customer service in transportation and logistics operations.

If you belong to any of these industries and already have an app, can hire an ML developer in India to integrate machine learning in Android app. 

What Popular ML Integration Tools Can Be Used in Android Apps?

Several popular tools and frameworks are used for integrating machine learning into Android apps. Here are some of the most commonly used ML tools developers use in various Android apps. 

1. TensorFlow Lite

TensorFlow Lite is a lightweight version of the TensorFlow framework specifically designed for mobile and embedded devices. It allows you to run pre-trained machine-learning models on Android devices efficiently. TensorFlow Lite provides various tools and APIs to load, run, and optimize machine learning models on Android.

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2. ML Kit

ML Kit is a mobile SDK provided by Google that makes it easy to integrate machine learning into Android apps. It offers a wide range of ready-to-use machine learning features, including text recognition, image labeling, face detection, barcode scanning, and more. ML Kit provides both on-device and cloud-based processing options, making it versatile for different use cases.

Still, you can check out the quick key highlights of ML Kit:

• Its ready-to-use machine learning models include text recognition, image labeling, face detection, barcode scanning, and more.
• Offering on-device inference and providing real-time and offline capabilities without relying on server-side processing.
• Providing cloud-based APIs for complex machine learning tasks, such as cloud text recognition and cloud translation, enabling powerful capabilities with minimal effort.
• Seamless custom model integration to bring your own models trained with popular frameworks like TensorFlow and deploy them in your Android app.
• Offering cross-platform support to leverage its capabilities across multiple platforms consistently.

ML Kit empowers developers with powerful machine learning capabilities, enabling them to enhance their Android apps with intelligent features and deliver exceptional user experiences.

3. PyTorch Mobile

PyTorch is a popular deep learning framework, and PyTorch Mobile allows you to deploy PyTorch models on mobile devices, including Android. It provides tools for converting PyTorch models to mobile-friendly formats and supports on-device inference. PyTorch Mobile is known for its flexibility and ease of use, particularly for developers already familiar with PyTorch.

4. Caffe2

Caffe2 is another deep learning framework that supports mobile deployment, including Android. It offers efficient execution of pre-trained models on mobile devices and provides tools for model optimization and conversion. Caffe2 is particularly well-suited for deploying computer vision models in Android apps.

5. Scikit-learn

While scikit-learn is primarily a machine learning library for Python, it can still be useful when integrating machine learning into Android apps. 

You can train your models using sci-kit-learn in Python and then export them to a format compatible with Android. This approach allows you to leverage Sci-kit-learn’s extensive machine-learning algorithms and preprocessing techniques. To make the best out of this framework, you can hire Android app developers in India skilled in integrating ML into Android apps.

These are the few most popular choice of Machine learning tools that help in simplifying the ML integration procedure. Though all tools have their own pros and cons, therefore the final choice of ML tools are depending upon the app requirements. 

So now you know the popular tools to integrate Machine Learning in Android apps, but how to integrate it?

Let’s jump on to the tutorial to apply ML in the Android app…

How To Deploy Machine Learning Model In Android Apps?

Appling ML into an Android app is quite simple but requires hiring a Machine learning developer in India who can handle the project’s complexity. Integrating machine learning in an Android app involves several steps. 

So if you are all set to take your Android app to the next level, you need to check out this simple step-by-step guide to integrate Machine Learning in Android app, along with code commands and explanations for each step:

Step 1: Set up the Android Development Environment

To begin, make sure you have Android Studio installed on your machine. Android Studio provides a comprehensive development environment for building Android apps.

Step 2: Create a New Android Project

Open Android Studio and create a new Android project. Choose a suitable project name, package name, and minimum SDK version based on your requirements.

Step 3: Add Required Dependencies

In your app-level build.gradle file, add the necessary dependencies for machine learning. One popular library is TensorFlow Lite, which provides tools and models for running machine learning on Android devices. 

Add the following lines to your dependencies block:

implementation ‘org.tensorflow:tensorflow-lite:2.5.0’

implementation ‘org.tensorflow:tensorflow-lite-support:0.1.0’

Step 4: Prepare Your Machine Learning Model

Before integrating your machine learning model into the Android app, you need to convert it into a format compatible with TensorFlow Lite. 

Typically, models are trained using frameworks like TensorFlow or PyTorch. Once you have a trained model, convert it to the TensorFlow Lite format using the TensorFlow Lite Converter.

Here’s an example command to convert a TensorFlow model to TensorFlow Lite:

tensorflow.lite.TocoConverter \

  –input_model=my_model.pb \

  –output_file=my_model.tflite \

  –input_format=TENSORFLOW_GRAPHDEF \

  –output_format=TFLITE \

  –inference_type=FLOAT \

  –input_type=FLOAT \

  –input_arrays=input \

  –output_arrays=output

Step 5: Include the Model in the Android Project

Copy the converted TensorFlow Lite model (e.g., my_model.tflite) into the app’s assets folder. This folder contains static files bundled with the app.

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Step 6: Load and Run the Model in the Android App

Now, it’s time to write the code to load and run the machine learning model in your Android app. 

Create a new Java or Kotlin class and add the following code:

import org.tensorflow.lite.Interpreter;

import java.nio.MappedByteBuffer;

import java.io.FileInputStream;

import java.nio.channels.FileChannel;

public class MLModelExecutor {

    private Interpreter tfliteInterpreter;

    public MLModelExecutor() {

        try {

            // Load the TensorFlow Lite model

            MappedByteBuffer modelFile = loadModelFile();

            tfliteInterpreter = new Interpreter(modelFile);

        } catch (Exception e) {

            e.printStackTrace();

        }

    }

    private MappedByteBuffer loadModelFile() throws Exception {

        FileInputStream fileInputStream = new FileInputStream(“path/to/your/model.tflite”);

        FileChannel fileChannel = fileInputStream.getChannel();

        long startOffset = 0;

        long declaredLength = fileChannel.size();

        return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);

    }

    public float[] runInference(float[] inputData) {

        // Prepare input and output arrays

        float[][] input = new float[1][inputData.length];

        input[0] = inputData;

        float[][] output = new float[1][numOutputClasses];

        // Run the inference

        tfliteInterpreter.run(input, output);

        // Return the output

        return output[0];

    }

}

In the above code, the loadModelFile() function loads the TensorFlow Lite model file into a MappedByteBuffer, and runInference() performs the actual inference using the loaded model.

Read More: Top AI Development Trends in 2024

Step 7: Integrate the MLModelExecutor into Your App

To use the MLModelExecutor class in your app, create an instance of it and call the runInference() method with your input data. You can then process the output as per your application’s requirements.

That’s it! You have finally integrated machine learning into your Android app using TensorFlow Lite!

However, this is the most general way of adding Machine Learning in Android apps. We have explained the overview of ML integration in Android apps in basic steps that help businesses and developers understand how simple it is. If you want to streamline your development process, consider opting to hire dedicated Android app developers who specialize in integrating advanced ML capabilities. Depending on your specific requirements and the machine learning framework you’re using, there may be additional steps or variations in the implementation.

Conclusion:

How Does AppsDevPro Can Help You Integrate Machine Learning into Android App?

With the increasing popularity of machine learning in various industries, businesses from all across the globe are looking to upgrade their apps by implementing the latest technologies. So if you are looking to integrate machine learning in Android apps, then it’s time to hire an app developer in India. 

AppsDevPro is a leading platform to hire expert coders in India that help you build an app with future-ready technologies. So if you have any app development requirements or need to level up with the latest technologies, then you can drop a query below or contact us.

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