How to Deploy Machine Learning Models in Android Apps Using Python?
Want to incorporate machine learning models in your Android apps? You are in the right place in that situation.
The number of mobile devices and applications has increased exponentially in the last 10 years. The variety of ways to use machine learning models in Android apps has also increased with this development.
In this blog post we will show how to use Python to deploy machine learning models in Android apps. We also provide some tips on how to improve the performance of your models. So let’s get started.
The machine learning algorithm you want to use must first be created. You can find many pre-trained models online if you don’t already have one.
The next step once you have your model is to convert it to an Android compatible format. For this, you can use the Vector Transform – space. Once your converted model is converted, you need to include it in your app’s assets. To do this, make a new folder called “tflite” in your app’s src/main/assets folder.
Then, transfer your modified model to a tflite directory. To install and use your model, you now need to write some code.
Making a class that extends the TensorFlow Lite Interpreter is the first step. This section is responsible for loading your model and performing inference operations on it.
The next step is to create a function that reads in an image as input and makes inferences on it. The inference result will be returned by this function.
The final step is to create a discovery function that uses inference. For example, you can use discoveries to classify an input image.
I’m done now! Now you understand how to use Python to implement machine learning models in Android applications.
