How Machine Learning transforming the landscape of Mobile App Development

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Marc Rothmeyer

Digitization has caused the world to change its paradigm by a few degrees. You agree on the fact or not. But the major disruption that has been caused here is through Smartphones and Mobile applications. Since then, many frameworks have been introduced to develop apps, like React Native, Xamarin, Flutter, Ionic, Adobe PhoneGap, Framework 7, and more.

But the introduction of Machine Learning in the mobile app development ecosystem has turned the table forever. Now with Machine Learning abilities, the app can easily collect data, analyze it, and draw insights from it through deep analysis through continuous learning ability. 

Since the induction of Machine Learning, it has gained a lot of traction in the technology landscape, especially because of its subfield of AI (Artificial Intelligence).

Now, app developers around the world are pretty much attentive towards the concept of ML. 

They are trying hard to develop smart applications with minimum code, less data and want to achieve data processing ability in real-time. Fortunately, thanks to the cognitive learning and pattern recognition ability of machine learning, developers have successfully harnessed the technology in the development process. 

Here are some highly proficient ML frameworks that you should certainly consider:

Google TensorFlow

This is a framework developed by Google, and it is proficiently used to create models based on Deep Learning. Deep Learning is a subset of ML that harnesses Artificial Neutral Networks to make the machines learn progressively. 

It is traditionally based upon a computational graph that forms a nodal network.

In this framework, every node represents a function, which can be a simple or complex operation. If you look at the Google services such as Google Photos, Google Music, Google Search, Google Recognition, they all use to show relevant suggestions, all that happens because of Tensorflow. 

This framework is so flexible and mature so that app developers can use it on various platforms, like iOS, Android, Windows, and so on. 

Amazon ML Service

Amazon ML Service

Now, it comes to the ML framework designed by none other than the tech giant Amazon. Amazingly, it offers developers with wizards and visualization tools to develop machine learning models without indulging in any complicated algorithms.

Without using any custom prediction generation code, APIs can give automatic prediction through this model. On the same lines as Google’s TensorFlow, this model can work equally efficiently with iOS and Android operating systems.

Core ML by Apple

Core ML by Apple

The next model in the list was developed by Apple and widely used across a wide range of Apple products, which include Quick Type, Camera, and Siri. This tool strictly follows the machine learning model to develop apps and think the way a human developer thinks. 

With some amazing features like face tracking, barcode scan, object tracking, text identification, and of course, face detection, this framework is widely used to design highly efficient computer vision ML features in mobile apps.  

This framework comes with some additional features which make it a complete ML tool in the real sense, like language processing, Lemmatization, tokenization as a part of Natural Language Processing APIs to get a better understanding of a given text. 

Microsoft Cognitive Services

Microsoft Cognitive Services

Microsoft Cognitive tool comes as a framework for machine learning and offers algorithms on Deep Learning complementary. There are many widely used services provided by Microsoft, like Skype, Bing, Cortana, and Xbox. These all are developed by using the toolkit of Microsoft Cognitive Services.

It allows developers to use multiple languages like C++, Python, Brain Script, which are some of the most used computer languages. 

Today, there are some APIs and other cognitive services that are harnessed to develop smart applications for Linux and Windows interface.

For instance, Computer vision API not only identifies the image but identify the content in it and can generate tags for other information. 

For instance, the Content Moderator API efficiently tracks, flags, and filter out irrelevant or offensive content. Now Face API assists face algorithms to enable face recognition feature.

Caffe Deep Learning Framework

Caffe Deep Learning Framework

This is an open-source ML framework by Berkeley AI Research and other community contributors at UC Berkeley. Among Convolution Neural Networks, it is known as one of the most usable frameworks that allow the recommender system, machine vision, image classification, and much more. 

Its prominence establishes from its pre-trained ML model, Model Zoo, which can perform multiple tasks without a flaw. 

But this framework has a limitation that it can’t perform operations related to computer vision like text, time, or sound. 

A Road to the Conclusion

ML was always destined to bring massive changes to the way businesses function, and the way data has been processed. Now ML, with its subset AI is delivering real fortune to every industry, and mobile app development is no exception here. Like, we at Mobcoder harnessing every aspect of Machine Learning in our app development process. Connect us to know how you can integrate ML into your workflows. 

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