Techniques of machine learning in android apps
According to machine learning documentation the machine learning is a programming technique that provides your apps the ability to automatically learn and improve from experience without being explicitly programmed to do so. This is especially well-suited for apps that utilize unstructured data such as images and text, or problems with a large number of parameters such as predicting the winning sports team.
So we know android supports a wide variety of machine learning tools and methods: like we have an ml design guide, Google’s turnkey machine learning SDK Ml kit and 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 app.
Here is some pre-trained models for mobile App:
Like we have TensorFlow Hub who contains a large repository of TensorFlow Lite models from Google and the wider research community and that are optimized to run on Android.
- ML Kit Image Classification Collection which is designed to work with ML Kit Image Labeling and Object Detection and Tracking APIs.
- Android Studio ML Model Binding Collection contains models that are compatible with Android Studio 4.1 beta’s ML model binding feature.
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According to IBM
Machine learning is a branch of Artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
IBM has a rich history with machine learning. One of its own, Arthur Samuel, is credited for coining the term, “machine learning” with his research (PDF, 481 KB) (link resides outside IBM) around the game of checkers. Robert Nealey, the self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962, and he lost to the computer. Compared to what can be done today, this feat almost seems trivial, but it’s considered a major milestone within the field of artificial intelligence. Over the next couple of decades, the technological developments around storage and processing power will enable some innovative products that we know and love today, such as Netflix’s recommendation engine or self-driving cars.
Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. These insights subsequently drive decision-making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase, requiring them to assist in the identification of the most relevant business questions and subsequently the data to answer them.
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TensorFlow Lite
TensorFlow Lite is a popular open-source deep learning framework, which can be used on-device mobile inference. Following Apple’s announcement of Core ML, Google released TensorFlow Lite, the next evolution of TensorFlow Mobile, which promised better performance by being able to leverage hardware acceleration on devices that support it.
This framework from Google can run machine learning models on Android and iOS devices. Today, TensorFlow Lite is used on billions of devices across the world, and its set of tools are being used for all types of neural network-related apps, from image detection to speech recognition.
TensorFlow Lite enables the bulk of ML processing to take place on the device, utilising less intensive models, which do not have to rely on a server or data centre. Such models run faster, give potential privacy enhancement, consume less power, and in some cases, do not need an internet connection as well. On Android such as the latest versions of the device, TensorFlow Lite leverages specialist mobile accelerators through the Neural Network API, providing better performance while minimising power usage that is expected when training datasets.
Source: TensorFlow Lite
The API for calling the Python interpreter is tf.lite.Interpreter
Converting TensorFlow Keras model to TensorFlow Lite model:
converter = tf.lite.TFLiteConverter.from_keras_model_file(keras_file)
The user can deploy pre-trained Tensorflow Probability models, Tensorflow KNN, Tensorflow K-mean model on Android by converting the TF models to TF Lite (guide), and the converted model can be bundled in the Android App
Key Takeaways From TF Lite Announcement
TensorFlow Lite:
- Run custom models on mobile platforms via a set of core operators tuned for this task.
- A new file format based on FlatBuffers.
- A faster on-device interpreter
- TensorFlow converter to convert TensorFlow trained models into Lite format.
- Using TensorFlow Lite cuts down the size of the models by 300 KB which allocates faster deployment.
Sparkfun Development Board:
- Uses extremely low power, less than 1mW in lot of cases.
- A single coin battery can run for many days.
- Runs entirely on-device
- Uses 20KB model
- Uses less than 100 KB of RAM and 80 KB of Flash.
Coral Development Board:
- Provides on-device machine learning acceleration.
- Uses Google Edge TPU, which does not depend on the network connection and can perform tasks like object detection under 15 ms. For instance, a shop floor personnel with no prior knowledge of machine learning can use the device just by training within seconds thanks to Edge TPU and perform object detection tasks.
- Runs inference with TensorFlow Lite.
The following state-of-the-art research models can be easily deployed on mobile and edge devices:
- Image classification
- Object detection
- Pose estimation
- Semantic segmentation
Check the TensorFlow Github repository here.
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