15th Aug 2022

The Impact of Machine Learning on Android Apps

Machine Learning, Mobile App

Written By, Jyotiranjan Mishra


Machine learning(ML) is a technique in which apps are able to work and learn by using algorithms and statistical models without any explicit programming. Machine learning is a growing technology and it gives enterprises business operational patterns and smooth user experience as well as enables the development of new applications.

Machine Learning for Android apps

Machine learning offers a lot of innovative transformations for developers. Nowadays, people want more personalized experience. ML can help developers personalize their mobile application according to the user’s vision by creating a quality app and making the users spend more time with the application. Therefore machine learning is growing rapidly and there are some more applications currently under development as well as launched in the market with beta version.

For Example:

  • Automatic language translation
  • Image recognition
  • Speech recognition
  • Stock market trading
  • Email spam and malware filtering
  • Traffic prediction

Best Machine Learning Frameworks

Evidently there is no best ML framework, your choice of framework will depend on the kind of application and data you are using. Factors to consider can be scalability, deployment, data processing, and other variables that add value to the performance of the application. However These are some popular frameworks

  1. TF Hub
    Tensorflow is a publicly accessible framework developed by Google. It is based on JavaScript and now this framework is used widely for machine learning. It provides free APIs for developers to build and train models. There are some adaptable features such as a regression model, a library for programming, neural networks and it is well suited to write algorithms for software. You can use it by script tags and NPM installation.
  2. Sci-Kit Learn
    Scikit-learn is specially used by python developers and it provides an extensive open-source library. It is a hugely applicable framework for mining and analysis of data. This framework provides a platform for algorithm, dimensional reduction, clustering and model selection, regression analysis and classification models.
  3. PyTorch
    It is grounded on Torch and Caffe2 with a lot of options for optimizing algorithms. Used by IBM and Meta, it supports cloud based software development and is suitable for designing neural networks and computational graphs. &nbsp
  4. Apache mxnet
    It was developed by Amazon for AWS. Mxnet is distributed on a cloud infrastructure and is scalable across several GPU and servers. It supports languages like JavaScript, Python, Julia, C++, Scala. Some big tech companies like Microsoft and Intel support Apache mxnet.
  5. H2O
    It is an open source framework developed for decision support system processes. The current version of H2O is H2O-3 that is compatible with Java, python scala, R. It integrates with Hadoop and Spark.

Machine Learning Libraries in Kotlin

Stackoverflow stated that Kotlin can be considered ‘the most preferred’ programming language by the tech giants, Securing 4th position among the other globally famous languages, Tech-enthusiasts are profound of this particular language, So Let’s throw some light to the top machine learning libraries in kotlin

  1. Kotlin Statistics Library
    This ML library is used for analysis of the data and statistics with object oriented programming concepts that makes the code legible and intuitive. The library has many helpful extensions which are used to perform production statistics and exploration in a kotlin idiomatic way. Kotlin statistics also contains few clustering algorithms.
  2. Krangl Library
    This library is mostly used for data wrangling. Krangls provides support for grouped operations and is also used for read and compressed tsv ,csv, json or any delimited format. It is inspired by dplyr for R, Kotlin stdlib, pandas and it mimics the API of dplyr. It can filter, transform, aggregate and reshape tabular data.
  3. Koma
    It is used to create a scientific programming environment for Kotlin that is similar to MATLAB and NumPy. This is used for writing numerical applications.The applications can be utilized on JVM, Js and also in native platforms.
  4. Kravis Library
    Kravis is a ML library for data visualization in kotlin. To create an extended range of plots using ordered sets of verbs, it implements a grammar. From docker to remote web services it works with numerous integration backends.
  5. Lets-Plot Library
    It is an open source plotting library and used for statistical data using the Kotlin programming language. The plotting functionality can be obtained as a JVM library, native python extension.

How to make a Machine Learning application

Creating machine learning is an iterative procedure depending on the type of solution and model you would like to approach. It should effectively answer the present issue and provide a scalable solution altogether. You need to gather, clean and filter data, feed the results and utilize the model for the required answer. In Android these technical aspects matter a lot as it can potentially utilize a wide variety of machine learning tools.

To develop and integrate Machine Learning into the android apps Google has provided two guide sites:

For best practices and solution to tackle common ML problems, there are 4 steps for developing ML apps for android

  1. Design Patterns
    Design patterns are a way of capturing problems and providing solutions using well proven ML designs. According to Wikipedia, ”For designing applications, design patterns are accredited as the best procedure that the developer can use to solve common problems”. Solving common problems through design patterns is an efficient way used by developers who are dedicated to incorporating Machine learning into everyday life.

    There are a bunch of unique challenges like quality of data, concept drift, duplicability, robustness, bias and much more that influences the result design. Registering these problems and solutions is an excellent way to transfer knowledge. and it even up to machine learning.
  2. Build and Train a Machine Learning Model
    Machine Learning requires a trained model that can perform a specific task like recognizing, classifying and prediction for some input. You can either make a model from scratch or customize an existing model. For model creation and training you can use cloud infrastructure or a development machine.In this step at first the data is stored in a dataset in a different format like matrix by regression and classification. After that data analysis occurs through different approaches.ay life.

    There are mostly three approaches i.e. Descriptive statistics, Data visualization, Data shaping. After that the Data processing and Data splitting occurs. Data processing is also known as data cleaning, data wrangling and data munging. After that Model building occurs in which various algorithms are used to develop the model.

    Machine Learning algorithms could be broadly classified into 3 types:

    • Supervised Machine Learning Algorithm
    • Unsupervised Machine Learning Algorithm
    • Reinforcement Machine Learning Algorithm
  3. Inference
    After successful training to perform tasks of the machine learning model, it’s time to use them. This process is called Inference. Depending on the composition of the input image there are two ways to deploy your model after you train a model with tensorflow lite or download from TenserFlow hub.

    1. A object or scene you are trying to detect which takes input image
    2. Detect a particular object from many object in the frame
      ML Kit for Object Detection and Tracking
  4. Deployment
    Deployment is the procedure of positioning and acquanting your machine learning model for the use on Android App.
    There are three primary ways:

    1. Include the ML Model in Android App
      The model is deployed like any other asset in the app and updating the model requires updating the app. In the app’s APK file, developers can add their ML model.
    2. Provide the ML Model at runtime
      In your app you can update your model independently and this also makes testing easier.
    3. A combination of both
      Developers can create a starting version of their machine learning model with their android apps so that users can get the beta version of the model while developers productively update the model and keep rolling out new upgrades.
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Written By,

Android Developer at Yudiz Solutions Ltd