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Build self-reliant smartphone apps using 5 best machine learning tools for developers

Build self-reliant smartphone apps using 5 best machine learning tools for developers

Machine learning is practically changing the way we interact with our smartphones today. Technology is gradually making headway towards automation in rocket speed triggering user expectations to exert more pressure on mobile application developers to keep up with the market trends.

Machine learning technology which is considered a subfield of the Artificial Intelligence has empowered the smartphones, tablets and other such devices to self-learn and react in real-time without any human intervention. This is not exactly a brand new concept but has gained popularity in the recent times when mobile application development companies have plunged into the competition to design ML enabled applications aptly reflected in a few examples that can be cited here:


• Snapchat Filters
• Carat App
• Dango
• LeafSnap and much more.


Also Read: 6 Android app makers for beginners


The mobile app development environment offers convenient tools and kits to aid budding developers in designing individual mobile applications. We do have the free Android app development tools and such for iOS applications as well.

Likewise, machine learning frameworks are introduced as well to aid developers in coming up with intelligent applications devoid of big data and use of minimal lines of codes.


Also Read: 5 Cloud based tools for mobile and web apps


Let's dig deeper into the 5 best machine learning frameworks to aid developers in designing smartphone applications.


1. Tensorflow by Google- A framework for creating Deep Learning models, forming a class of Machine Learning, using Artificial Neural Networks to a device the systems to self-learn and improvise a task by referring to examples in the absence of task-specific codes. Tensorflow occupies a major part of various Google services that are offered to us today and some of which are quite popular such as Google Photos, Google Recognition, Google Search, and few others.

Google Translate application makes use of the ML framework at the backend to offer the instant visual translation.


2. Core ML by Apple- Core ML is a Machine Learning framework designed specifically to be used across Apple products like QuickType, Siri, and Camera launching during the WWDC 2017 event to cater smart iOS app development process. Developers design machine learning features incorporating them into the iOS apps inducing them with capabilities to perform tasks that most human eyes do. Few supportive features include object tracking, face detection, text detection, face tracking, bar code detection, and lot more.

Also, Core ML offers Natural Language Processing to read the text thoroughly, using language identification, part of speech, lemmatization, and related features.


3. Amazon ML- Within ML framework, Amazon enables developers to utilize the visualization tools and wizards to develop ML models without hampering the algorithms or technologies thereby ensuring its implementation at all levels of apps by the mobile app developers. There is minimal to practically aero cost investment for hardware or software.

Developers are assisted with platform-specific guides to guide them about the pre-requisites, client creation, offer examples, and much more.


4. Caffe Deep Learning- Caffe is one of the popular tools, also known for its Model Zoo, has been designed and pre-trained on ML model to offer ease in performing different tasks like image classification, machine vision, recommender system etc. However, this framework does not cater non-computer vision tasks such as sound, time series, or text.


5. Diffblue- DiffBlue is a simple yet useful code automation platform that aims in an automated location of bugs, refractor code, perform test writing and find and fix weaknesses in code. Such a tool aids in app development process inducing the application with artificial intelligence to conduct the said task devoid of external coding or human intervention
There are few others but we cannot miss out the contribution made by Microsoft here. The cognitive toolkit was already available for ML app creation and presently more such tools have been added to the kitty.


• Cognitive Toolkit
• Azure Machine Learning Experimentation service
• Azure Machine Learning Workbench
• Azure Machine Learning Model Management service


Time to wrap up
As mentioned earlier we do have quite a few applications for both mobiles as well as web space categorized into different domains that make use of this technology. Beyond mobile application development service, this technology is implemented in other sectors such as data mining, finance, e-commerce etc.

The top-rated mobile app development companies in Dubai have also penetrated this competitive sector to exploit the benefits offered by the machine learning and artificial intelligence enabling their end users has a taste of the futuristic hi-tech and multitasking device.

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Fluper LtdFluper Ltd
Category: Mobile App DevelopersCompany profile: Fluper Ltd. is an authentic, certified and top rated mobile application design and development company with engineers expertising in native application development, customized applications provisioning end-end 360degree mobile app solutions. We have specilized in Android, iOS, iPad, Tablets and Wearable App Development Services. We have been working in the IT Mobile App Domain and Verticals since 2013 providing following services: #Enterprise Applications: Business, Banking, Finance, Human Reso ...