Android Machine Learning
By: Kayvan Kaseb
Tags: Machine Learning, Android App Development, Android Development, Android, Android Machine Learning

The picture is provided by Unsplash

As you know, a growing interest to Machine Learning forced the companies to focus and invest on it. Machine Learning (ML) is the major of computational science, which focuses on analyzing and interpreting patterns and structures in data to enable learning, reasoning, and decision making outside of human being interaction. Furthermore, Machine Learning is a programming approach that provides your Android apps the capability to learn and improve automatically from previous experience. As a result, Google has designed and implemented a number of advanced features and tools for Android Machine Learning. This article aims to discuss some main concepts and advanced features that have been recommended for Android developers.

Introduction and Overview

Basically, Machine learning (ML) is a programming approach that provides your Android apps the ability to learn and enhance automatically from experience without being explicitly programmed to do so. Recently, Android has a wide range of Machine Learning tools that are available for everyone: designers, product managers to engineers, mobile developers, and Machine Learning experts. Now, Android is a platform, and one of the ways you, which you can measure the success of a platform is through the great achievements that you build on top of it. Today, Android runs on 2.5 billion devices, and a number of them are already using on-device ML. Also, on-device Machine Learning is no longer a technology of the future because it is accessible for using right now. For example, it has been mentioned that more than 25,000 applications on Android and iOS make use of ML Kit’s features.

In other words, the modern mobile phones have enough capacity to process as much as traditional computers can, and our phones and tablets are powerful enough to run applications, which can learn and interact in real time. Machine Learning(ML) is helping us build smart apps. For instance, the Google Apps prediction for the next apps you are likely to use, and a mobile app, which solves the world’s biggest problem for finding the perfect emoji. Besides, Smart Assistants, Snapchat Filters, Smart Reply, and Google lens are some of the other products of utilizing Machine Learning on devices. A number of SDKs exist on mobile phones that help run Machine Learning on mobile devices. Some of them includes software development kits developed by popular companies such as Amazon, Intel, and Microsoft, and also Google has developed the ML Kit. The following sections of this essay will consider some main concepts and new features that have been recommended by Google for Android development.

The definition of Machine Learning (ML)

In fact, the major of Machine Learning is concerned with the question of how to construct computer programs that improve automatically with experiences. Thus, your answer is in your data. In short, Machine Learning is considered as a subset of AI, which uses statistical methods to enable machines to enhance with experience. It enables a computer system to make decisions to carry out a certain task. These programs or algorithms are designed in such a way that they can learn and enhance over time by observing new data. The main objective of Machine Learning is to derive meaning from data. Therefore, data is the key to unlock Machine Learning. The more qualified data ML has, the more accurate the ML algorithm becomes.

Machine Learning (ML) is the science of computer algorithms that improve automatically through past experiences.

To make predictions or decisions without being explicitly programmed, Machine Learning algorithms build a model based on examples that is called training data.

Machine Learning is the extraction of knowledge from data.

Additionally, there are some differences between Artificial Intelligence and Machine Learning. The goal of Artificial Intelligence is to create a machine that can mimic a human mind, and it requires learning capabilities as well. However, it is more than just about learning; it is also about knowledge representation, reasoning and abstract thinking. In contrast, Machine Learning is solely focused on writing software, which can learn from past experiences. Besides, Machine Learning is more closely related to Data Mining and Statistics than it is to Artificial Intelligence.

Machine Learning allows us to solve certain kinds of problems in very elegant ways. The important point is that why do we use Machine Learning in practice. For example, you had to write code to detect if someone was walking. Besides, if you had access to his speed, you must write a fairly simple bit of code to specify a metric for speed that shows walking in reality. Extending this situation to running seems straightforward enough. In addition, if we wanted to cover biking, we just need to add another boundary condition to our code. At the moment the question is what about other types of activities, for instance, golfing?

In fact, golfing includes a lot of walking, but there is also carrying and swinging a bunch of clubs. So, how do we explain this condition? This is where Machine Learning can help us effectively. In traditional programming, we express rules in languages, such as Kotlin, Java. Then, we apply those rules to problem of interest. In Machine Learning, the paradigm is somewhat different. Instead of writing a bunch of rules, we take a lot of label data like photos of people engaged in different types of activities with corresponding labels for those activities. Besides, if we feed this data to our Neural Network, it will learn the business rules for us without requiring to define them. In short, Machine Learning has proven to be remarkably effective at solving a number of various kinds of issues like activity detection.

Machine Learning in Android Development

As you noticed, Machine Learning is a programming approach that provides your Android apps the capability to learn and improve automatically from experience without being explicitly programmed to do so. Furthermore, this is well-suited for Android apps that use unstructured data such as images and text, or problems with large number of parameters like predicting the winning sports team. Currently, Android provides a wide variety of machine learning tools and methods: The ML design guides, Google’s turnkey machine learning SDK ML Kit, TF Hub for finding pre-built cutting edge models, TF Lite Model Maker to train an existing model with your own data, and ML Kit custom models and Android Studio for integrating these models into your Android app in an appropriate.

To develop ML apps for Android, you will face four main tasks as follows:

Design: identifying product goals and providing ML design patterns to achieve them. Build and Train: building the ML model, or training pre-trained model from Google. Inference: the process of using a machine learning model, which has already been trained to accomplish a certain task. Deploy: Installing and updating ML models for your Android app.

Guidelines for designing Android apps

Initially, there is no difference compared to other advanced technologies. Your product designers and engineering all combined to decide what kind of problem are we trying to solve with our product. So, it is not a single effort. It is an extremely iterative process when you have a new technology. You want to test the boundary, and you want to collaborate. In addition, you want to observe how the user really use your product. This means Design, Product Management, and Software Engineering play key roles in this process. Google has produced two guides in this field of study as follows:

The People + AI Guidebook : In this guidebook, there are six different sections to help you structure your conversation with your designers and product managers. In overall, it offers best practices to help your team make human-centered AI product decisions.

2. The The Material Design for Machine Learning spec : this guideline includes of following sections:

First, new Material Design guidelines for ML use cases on mobile.

Second, enabling great end-to-end mobile UX powered by ML.

Third, source code available for Object Detection and Tracking and Barcode Scanning.

The key decision in Android development

As a matter of fact, a key decision you should address in Android development is inferencing will run on the device, or use a cloud service that would be accessed remotely. To make this decision accurately, you should consider some parameters as advantages or disadvantages. For instance, for on-device approach you will face some benefits as follows:

Privacy: In many cases, it is more appropriate to just keep everything on device. Therefore, we do not have to worry about how the data is processed in the cloud.

2. Offline: on-device Machine Learning works when there is no connectivity, or when the network is patchy. For example, if you spend any time underground trains, you will know that this could be useful.

3. Low latency: by running our processing on device, we can be able to remove network latency out of the equation altogether. Also, coupling this with the latest advancements in hardware acceleration, we can give users truly real-time experiences across a wide range of Android devices.

ML Kit for app developers

As a matter of fact, ML Kit SDK is a new product from Google, which was presented in 2018. ML Kit is a software development kit that makes it possible for developers to simplify the integration of machine learning models into their mobile apps. In addition, even a junior developer can address this task easily. Another point is that it makes your iOS and Android apps much more engaging, personalized, and useful with solutions that are optimized to run on device. As it was mentioned, more than 25,000 apps on Android and iOS make use of ML Kit’s features.

A new SDK that brings Google’s Machine Learning experience to mobile developers in a powerful, easy-to-use package on Firebase.

ML Kit has some features that can be categorized in three sections:

Vision: Video and image analysis APIs to label images and detect bar-codes, text, faces, and objects. Natural Language: Natural language processing APIs to identify and translate between 58 languages and provide reply suggestions. Custom: Build your own models using AutoML Vision Edge, or bring your own TensorFlow Lite models, run experiments, and deploy them in production with ML Kit.

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Each category has some features. For instance, Vision has some features as Base APIs: Barcode Scanning, Face Detection, Image Labeling, Object Detection, Landmark Detection, and Text Detection.

TensorFlow Lite

Basically, TensorFlow Lite is a set of tools to help programmers run TensorFlow models on mobile, embedded, and IoT devices appropriately. In fact, it provides on-device machine learning inference with low latency and a small binary size. In other words, TensorFlow Lite is designed to make it easy to run Machine Learning on devices, at the edge of the network, instead of sending data back and forth from a server. TensorFlow Lite includes two main components:

The TensorFlow Lite interpreter : it runs (optimized models) on many different hardware types, such as mobile phones and microcontrollers.

2. The TensorFlow Lite converter : it converts TensorFlow models into an productive form for use by the interpreter, and can be able to introduce optimizations to enhance binary size and performance.

Eventually, the steps for using TensorFlow Lite contains of the following steps briefly:

Picking a model Converting the model Deploying to your device Optimizing your model

AutoML Vision Edge

This new feature from Google can build custom image classification models from your own training data. In fact, if you are planning to recognize contents of an image, one option could use ML Kit’s on-device image labeling API. However, if you require a more specialized image labeling model, you can be able to utilize Firebase ML and AutoML Vision Edge to train a model with your own images and categories. For instance, a model to distinguish between species of flowers or types of food. The custom model is trained in Google Cloud, and then it can be used fully on the device.

In conclusion

At a glance, Machine Learning is a programming approach that provides your Android apps the capability to learn and enhance automatically from previous experience. As a result, Google has designed and implemented a number of advanced features and tools for Android development. This article considered some main concepts and advanced features that have been suggested for Android developers by Google.