Mobile app development evolves every year, with users demanding innovation combined with advanced features. In a time when mobile phones have become the core of business development, apps need to meet the criteria. The traditional apps do not support new devices. Seeing the technological advancement in every field, app development companies build software that updates existing apps to fit well into the latest gadgets. It wouldn’t be incorrect to say the advancement in mobile applications and smartphones has brought a paradigm shift in our lives worldwide. Hence, catering to the ever-increasing demands with enhanced user experience.
Mobile apps have built an ecosystem of their own. They contribute to the digitalization of businesses, automating business workflow for increased efficiency, effectiveness, scalability, etc. The next-generation solution and strategies combined with technology facilitate enterprises to reap the most out of the existing resources. They deliver next-level performance. Artificial intelligence, machine learning, augmented reality, and blockchain technologies provide futuristic solutions to companies. Moreover, they solve emerging problems and provide a competitive advantage. Many mobile application companies cater to the ever-increasing demand for smart devices. They provide one in all solutions to users. Let’s focus on machine learning frameworks for improved functionality.
What is Machine Learning Mobile App Development?
Machine learning is considered the subset of artificial intelligence. It pledges to provide automation while maximizing the benefits and providing feature-rich applications. Machine learning helps develop robust software, applications, and devices to minimize the efforts. Hence, mobile app development companies build ML apps with ML frameworks. They lead to intelligent on-device processing, easy coding, and an engaging interface.
A few machine learning frameworks have revolutionized the mobile app industry to cater to a vast audience.
The 4 ML Frameworks
We all know the two types of mobile devices, Android and Apple. They are always in competition with Apple outdoing the other one in terms of its top-level features. Core ML is a machine learning Apple framework developed in 2017 to build flexible and scalable applications. Core ML gets integrated into Apple’s Siri, Camera, QuickType, etc. Apple is known for creating apps. These apps function as humans in some way or the other, and their computer vision feature is an example of it. The app enables face tracking, barcode detection, object tracking, and face detection to provide advanced features for its users. Moreover, the app framework integrates natural language processing APIs to understand the text better. Furthermore, it uses other features like tokenization, identification, etc.
The best part about the Core ML feature is it offers various ML models like MobileNet, Squeezenet, etc. They help develop smart apps for iOS users.
We have talked about an Apple mobile app development framework for your mobile apps. Now let’s focus on Android as well. The Tensorflow by Google is a unique framework created to build deep learning models quickly. If you do not know what deep learning is, then it is a class of machine learning. It uses Artificial Neural Networks (ANNs) to help machine learning improve its performance without task-specific coding. Unlike the Core ML framework, the computational graph-based framework comprises a network of nodes performing different functions. Whether simple or like the basic mathematical operations or complicated like the multivariate analysis.
Surprisingly, most of the Google services you use today get supported by Tensorflow. It enhances users’ experiences and increases satisfaction. Google Photos, Google Translate, Google Search, and Google Recognition are a few examples of this amazing framework. Furthermore, the framework lets developers build high-functioning apps for various platforms. Some examples include Windows, iOS, Android, Mac, and Linux. Moreover, Uber, Dropbox, Intel, Snapchat, Twitter, etc., are a few examples of Google TensorFlow-supported applications.
Caffe Deep Learning Framework
Since deep learning is a class of machine learning, it becomes essential to discuss it in the machine learning category. Secondly, the deep learning framework Caffe is an open-source framework developed by Berkeley AI research and community contributors. The feature-rich framework integrates Convolutional Neural networks (CNNs). Hence, leveraging users with machine visions, image classification, recommender systems, etc. Many of the companies or developers know it because of its most used pre-trained machine learning model. The Model ZOO, which performs various tasks for the developers.
The Caffe framework develops applications for Mac, Linux, and Windows platforms enabling users and developers to run different hardware and switch it between CPU and GPU with a single flag setting. Unlike other frameworks, it does not support computer visions to perform tasks like text, sound, and time series.
Microsoft Cognitive Services
Microsoft cognitive toolkit is another machine learning framework offering deep learning algorithms for developers to build robust apps for end-users. The unique mobile app development framework leverages developers with familiar programming languages like Python, C++, and Brainscript to develop futuristic mobile apps. Skype, Bing, Xbox and Cortana are a few examples of the most popular Microsoft services framework.
Developers can build innovative mobile applications for Linux and Windows using a few cognitive services and APIs.
4 Advanced APIS:
- Computer Vision: The computer vision API observes images and content to create meaningful information and generate tags.
- Content Moderator: The content moderator API helps users track, assess, filter, and flag offensive content to avoid unseen risks to your business. The unique API facilitates users with videos, images, and text. Moreover, this API can help companies monitor user-generated content on gaming, social media, and peer communication platforms.
- Emotion: The most crucial API emotion allows developers to use emotion recognition algorithms to create customized apps. It observes and integrates users’ facial expressions to enhance user engagement.
- Face: The face API lets the developers access the modern algorithms to facilitate users with attribute detection and face recognition features. Facebook is the best example of an app using Face APIs. It suggests similar faces to users for tagging purposes.
- LUIS: The Language Understanding Intelligent Services API enables developers with NLP installation features. Moreover, it helps your business apps understand the user requirements. It also supports intelligent features like chatbots and IoT devices.
In conclusion, mobile application development is not an easy task. It takes a long time to develop feature-rich, performance-oriented, and scalable applications for businesses. However, the mobile frameworks decide the success of your applications and how well it gets integrated into the system. Lastly, there are thousands of mobile app frameworks. But here we covered a few of the most used frameworks for more clarity.