How To Add Machine Learning To Your App

How to Apply Machine Learning (ML) in an Android App

Machine Learning is an application of Artificial Intelligence (AI) which empowers software to learn, explore, and envisage outcomes automatically without human interference. Machine learning has been used in numerous fields, and it is now aggressively serving to mobile application development.

There are multiple ways to apply machine learning in an Android app. The most suitable way relies on jobs or tasks you want to crack with the assistance of machine learning.

Machine learning algorithms c a n do the analysis of targeted user behavior patterns and have searching requests to make suggestions as well as recommendations. It is extensively used in mobile e-commerce applications. А video and audio recognition is even a type of ML used in entertainment domain like Snapchat.

It can also be used for face or fingerprint recognition to ease authentication. Otherwise, you can add a chatbot to your mobile application, which has turned popular with applications such as Apple Siri.

As per the research conducted by bccresearch, The global machine learning market totaled $1.4 billion in 2017 and is estimated to reach $8.8 billion by 2022. Machine learning vs artificial intelligence also a most debated topic for data analysts.

Technology professionals even optimize searching processes by enabling ML to an Android app. By adding a spelling correction, voice search, or a searching procedure for your targeted users will be turned more spontaneous and less bothersome.

Machine Learning for Mobile Apps

Mobile app developers have a lot to gain from innovative transformations that Machine Learning (ML) is offering across the industry. This is possible due to the technical capabilities mobile applications bring on table enabling smoother user interfaces, experiences, and empowering businesses with prominent features, such as delivering precise location-based suggestions or right away detecting chronic diseases.

People want their experience to be absolutely personalized these days. So, it is not enough to create a quality app, but you have to even make your targeted users stick with your mobile application.

Here, machine learning can help you. Machine learning technology can renovate your mobile application into the user's vision.

How to make a Machine Learning App

Making ML applications is an iterative procedure that involves framing the core machine learning issues with what is presently observed and what solution you want the model to foresee. Next, you need to gather, clean, and filter data, feed the results, and further utilize the model to produce forecasts of required answers for the newly generated data instances.

Some of the Top Machine Learning Applications -

1) Netflix

At Netflix, they utilize machine learning algorithms. It has made precise, personalized references by using linear regression and logistic regression and more such algorithms.

The Netflix application uses a diverse range of contents classified into variety, actors, user and critic's reviews, timespan, year and much more to offer to their audience. All this information goes into machine learning algorithms.

ML algorithms at Netflix are trained through user actions which track the behavior of its users. It monitors what TV shows I watch or what types of reviews I provide online. And machine learning algorithms familiarize to this user behaviors to offer exceedingly personalized content.

2) Tinder

We know tinder as a mobile application that helps in finding a dating partner. It uses machine learning algorithms to find a precise match. It uses pieces of information such as posted pictures by showcasing them randomly and does an analysis that how frequently they are swiped which helps the app to have a reordering of your photos by putting most observed ones foremost. This innovative feature boosts a user's chances of discovering an ideal match.

3) Google Maps

Google maps utilize machine learning to find a parking slot. It uses data analysis techniques to do this.

Google's researchers gather and study data from a very large sample of people. They ask them questions on how long and if they faced any difficulty to find vehicle parking. They fetch, aggregate and use this data by creating different training models from those who shared their location information.

Machine learning further applies processes over an Android mobile application by Tensorflow which is an essential ML framework.

How to Apply Machine Learning to Android

There is a number of machine learning frameworks available and we pick up here Tensorflow for example.

TensorFlow is an open-sourced a library of Google that is utilized in Android for implementing Machine Learning. TensorFlow Lite is used as a TensorFlow's lightweight solution for mobile devices. It enables on-device ML inference using a low latency which is why it is very swift. It is extremely good for mobile devices as it takes the small binary size and even backs hardware acceleration by utilizing Android Neural Networks API.

Using TensorFlow Lite in an Android application

Here is the summary of android TensorFlow machine learning example and how to apply Machine Learning to Android. To execute the model with the TensorFlow Lite, you will have to change the model into the model (.tflite) which is acknowledged by the TensorFlow Lite. The important stuff while using the TensorFlow Lite is to build a model (.tflite) that is poles apart from the standard TensorFlow model.

By attaining the model and the label file, one can initiate and label files in the Android application for loading the required model and predicting the output by utilizing the needed TensorFlow Lite library.

We have the experience of building an entire running sample application by using the TensorFlow Lite intended for required object detection.

Training a TensorFlow Model on Android

It can take a much longer time to train a TensorFlow model which needs a large quantity of data. However, there is a way to make this procedure much shorter without requiring huge GPU processing power and gigabytes of images. Transfer learning is the course of action of using a previously trained model and retraining it to build a new model.

You can do this training by following below steps -

• Step 1: Collect training data

• Step 2: Transform the data into required images

• Step 3: Create folders of images and group them

• Step 4: Retrain the model with the fresh images

• Step 5: Optimize the model for accessible mobile devices

• Step 6: Embed .tflite file into the application

• Step 7: Run the application locally and perceive if it detects the images

The difficulty of entry for using machine learning is becoming less significant. Many companies have created completely trained machine learning APIs that you can commence using instantly:

Some of the Highly Trained Machine Learning Services and APIs

• Google Play Services — Mobile Vision API

The primary group of machine learning services was created into the Google Play Services SDK. This conveys that any of the Android developers can utilize these services in their applications. Google's cloud Vision API is one of the examples which empower developers to use the Android camera to sense faces, check barcodes, and identify text.

• ML Rest Services — GOOGLE CLOUD ML APIs

The rest services are well trained and always ready-to-use for smart tasks. There are many of these REST services to pick from, with both free and paid options.

Google's ML platform comprises of translation, speech recognition, NLP and job listing APIs, in accumulation to the REST version. To initiate use of the Google ML platform, you will just require a Google Cloud Platform account for signing in and using the services.

The REST Vision API backs numerous sorts of requests incorporating information related to labels, text, image properties and much more.

Moving Forward

Machine learning services enable intelligent platforms to assist you in creating, training, and hosting the required predictive models. These platforms are supple and easy to use once you get hands on them. Few of the disadvantages are the overpowering landscape of diverse algorithms and configurations requisite to set a machine learning application right from the starting point. However, if you already have the latest knowledge of machine learning development, the advanced services deliver powered, and resourceful computing possessions to do the accurate analysis of data as well as have highly precise predictions.