Machine learning allows the software to learn, explore, and envision results automatically without human interference. Machine learning has been used in many fields and is now actively used in the development of mobile applications. In this article, I’ll walk you through how to build your Android apps with Machine Learning.
There are many ways to build Android apps with machine learning. The most suitable way is based on the jobs or tasks that you want to solve with the help of machine learning.
Why Android Apps Need Machine Learning?
Machine learning algorithms can analyze behaviour patterns of targeted users and receive search requests to make suggestions as well as recommendations. It is widely used in e-commerce mobile applications. Video and audio recognition is even a type of machine learning used in entertainment Android apps like Snapchat.
Machine learning can also be used for facial or fingerprint recognition to facilitate authentication. Alternatively, you can add a chatbot to your Android app, which has become popular with apps like Apple Siri.
According to research conducted by bcc research, the global machine learning market totalled $ 1.4 billion in 2017 and is expected to reach $ 8.8 billion by 2022. Machine learning versus artificial intelligence is also a hotly debated topic for data analysts.
Tech professionals even optimize research processes by enabling Android apps with machine learning models. By adding spell check, voice search, or search procedure, your targeted users will become more spontaneous and less annoying.
How Machine Learning Can Improve and Android Application?
Android developers have a lot to gain from the innovative transformations that Machine Learning offers in the industry. This is possible thanks to the technical capabilities that Android apps bring to the table, enabling smoother user interfaces and experiences and giving businesses top-notch functionality, such as delivering precise location-based suggestions or immediate detection. chronic diseases.
People want their experience to be personalized these days. So it’s not enough to build a great app, but you even have to force your targeted users to stick with your mobile app. Here, machine learning can help. Machine learning technology can transform an Android application in the user’s vision.
How To Build Android Apps with Machine Learning?
There are several machine learning frameworks available for building Android apps with machine learning. The best so far is TensorFlow. TensorFlow is an open-source library from Google which is used in Android to implement Machine Learning.
TensorFlow Lite is used as TensorFlow’s lightweight solution for mobile devices. It enables Machine Learning inference on the device using low latency which is why it is very fast. It is extremely good for mobile devices as it takes the small binary size and even supports hardware acceleration using the Android Neural Networks API.
Using TensorFlow Lite in Android Devices
Now I will walk you through how to use your TensorFlow models in Android apps using TensorFlow Lite. To run the model with the TensorFlow Lite, you will need to change the model to a model (.tflite) which is recognized by the TensorFlow Lite.
The important thing when using TensorFlow Lite is to create a model (.tflite) which is the opposite of the standard TensorFlow model. By reaching the model and label file, one can launch and label files in the Android application to load the required model and predict the output using the necessary TensorFlow Lite library.
Now let’s take a quick look at the process of training and deploying a machine learning model in Android apps. Training a TensorFlow model that requires a large amount of data can take significantly longer.
However, there is a way to make this procedure much shorter without requiring massive GPU processing power and gigabytes of images. Transfer learning involves using a previously trained model and recycling it to create a new model.
The process of making Android apps available with the power of machine learning algorithms involves:
- Collect training data
- Turn data into required images
- Create image folders and group them together
- Repackage the model with fresh images
- Optimize the model for accessible mobile devices
- Embed the .tflite file in the application
- Run the app locally and see if it detects the images
I hope you liked this article on how to serve android apps with Machine Learning models. Feel free to ask your valuable questions in the comments section below.