As mobile applications become a more integral part of our daily lives, the expectations for their functionalities are constantly increasing. Implementing machine learning and artificial intelligence is one of the ways to enhance their capabilities. This includes using multiple technologies such as H2O, Caffe, Apache Spark, and others.
TensorFlow is one of the most popular technologies used in machine learning for mobile apps because it’s open-source, includes many useful features, and is easy to use.
Read on to learn more about the implementation of machine learning with TensorFlow and its use cases.
Understanding TensorFlow for Mobile Apps
TensorFlow is a powerful and versatile ML framework that is very popular in the AI and ML communities. Developers use it to build, train, and deploy ML models efficiently. The framework’s adaptability makes it perfect for mobile app development, considering the limited resources and need for real-time processing.
TensorFlow Lite is specifically focused on devices with limited computing resources, such as phones, tablets, and other embedded devices. It enables on-device machine learning as the software is already adapted for Android and iOS.
Machine Learning with TensorFlow
TensorFlow machine learning allows developers to implement various algorithms, from image recognition to natural language processing. Here are some possible applications of the TensorFlow machine learning models:
- Image recognition: software like Google Lens uses TensorFlow for real-time object recognition to identify objects and landmarks and scan text from images;
- Natural Language Processing (NLP): software like chatbots, translation apps, and voice assistants use the framework to understand and generate human language;
- Recommendation systems: apps like Netflix use TensorFlow to analyze user preferences and suggest content based on multiple factors;
- Predictive text: tools like smartphone keyboards often use TensorFlow ML to provide accurate and context-aware suggestions when you’re typing.
There are also many other ways to use this framework as it includes dozens of ready-made solutions that can be fine-tuned for your project. Each has a comprehensive tutorial instructing you how to use it in your applications, making it easy to understand.
Pros of TensorFlow
TensorFlow on mobile apps includes multiple advantages. Some of them are:
- Versatility. The framework supports a wide range of machine learning tasks, making it suitable for diverse mobile app requirements.
- Community and support. The large and active community constantly provides updates to the framework, offering tons of resources, tutorials, and support for developers.
- Pre-trained models. You get access to a vast library of pre-trained models that save you time and money in creating new models.
- Cross-platform compatibility. The solution is compatible with both Android and iOS, allowing developers to work with large audiences.
- Real-time interference. Mobile apps can perform real-time interference without relying on cloud servers, reducing latency and improving user experience.
- Security and privacy. On-device inference enhances data security by reducing the need for continuous server communication.
Cons of TensorFlow
However, machine learning in TensorFlow also has some disadvantages. They include:
- Steep learning curve. Beginners might find it difficult to use the framework without any former knowledge of machine learning in mobile apps.
- Hardware variability. Using the framework for mobile applications might be challenging due to potential performance discrepancies with varying hardware capabilities.
- App size. Using TensorFlow can increase the size of mobile apps, making it a potential concern for apps with strict size limitations.
7 Steps to Implementing TensorFlow in Mobile Apps
It’s quite easy to implement ML algorithms with TensorFlow when following all the available tutorials and step-by-step guides from the official website. Here’s a quick guide to start using the framework:
- Install TensorFlow or TensorFlow Lite in your development environment using the framework’s latest version from the official website.
- Choose a pre-trained model that suits your app’s needs. Depending on your needs, you can choose anything from image classification to text analysis.
- Integrate the model into your app and make sure to optimize it for mobile platforms, considering the limited memory and resources.
- Collect data and train the model to fine-tune the selected pre-trained model with your data. This process is referred to as “transfer learning” and speeds up your work.
- Work with inference to make predictions and classifications within the application. You can run inference locally on the device, ensuring real-time responsiveness.
- Design the user interface to ensure the model fits your app’s concept effectively and provides a seamless user experience.
- Test and optimize the app to ensure it has maximum performance and doesn’t use too many resources on the device.
Remember to use the best practices for machine learning mobile apps to mitigate all the potential issues and develop a successful application.
Real-World Use Cases
There are many TensorFlow and TensorFlow Lite examples within software built by the world’s largest corporations. We’ve gathered some of the most prominent ones here.
Google: Enhancing Mobile Keyboards with Handwriting Recognition
Google launched its “Google Handwriting Input” feature that lets users handwrite text on Android mobile devices. To provide a seamless experience and eliminate the need for switching input methods, they integrated handwriting recognition into Gboard for Android.
Leveraging TensorFlow, they achieved high accuracy in handwriting recognition, supporting over 100 languages. The framework’s role didn’t stop there; it played a crucial part in optimizing recognition models for on-device inference, ensuring low latency, and reducing APK size.
Twitter: Improving Timelines with TensorFlow-Powered Ranking
Twitter launched a “Ranked Timeline” that prioritized important Tweets in a user’s feed using mobile app machine learning. TensorFlow was used to score each candidate’s tweet using a relevance model, predicting user engagement and promoting engaging conversations.
The model was trained on thousands of texts from different authors and users to contribute to its success. The adoption of TensorFlow unlocked improved model quality, faster iteration, and enhanced maintainability, ensuring a more relevant and engaging Twitter experience.
Future of TensorFlow in Mobile Apps
Considering that ML and AI are becoming an integral part of our lives, the future of TensorFlow in mobile apps looks promising. We can anticipate increased model efficiency and size reduction for better performance, a broader range of TensorFlow Lite models for various tasks, and enhanced privacy and security features for data protection.
Now you know what TensorFlow in machine learning is and why it is so important. This tool may be a real game-changer for your mobile application with its multiple pre-trained models and enhanced adaptability. Our machine learning services will help you develop any model and conduct data engineering & analysis for maximum efficiency. Let’s discuss your project today!