AI Engines for Mobile Developers and their Used Cases !!!
AI Engines for Mobile Developers and their Used Cases !!!
Integration of AI in the application is the latest trend that everyone is following. With classic examples of E-Commerce applications like Amazon, there are also many other apps that use AI to enhance the user experience. With AI as the buzzword today, most of these applications come with analytics tools and features that support predictiveness. To make it more viable there are some organisations that have made great ML Kits that can be used for mobile development. These ML Kits gives you the advantage of AI and a lot of features that you may not have imagined. Therefore, in order to check out some of the best AI Engines for app development read the article below.
About: It is one of the first Commercial grade Deep Learning Library for Developers. The library has been written in Java and is completely open-source. The great things about this library is that is can be integrated with Hadoop and Apache Spark. DeepLearning4J allows you to integrate your business with AI and make it capable of integrating it with distributed GPUs and CPUs.
- Library has been written in Java.
- Capable of creating a deep neural network.
- Integrate sequence to sequence autoencoders.
- Use Hadoop on top of distributed GPUs and CPUs.
- Can be used as a DIY tool for Java, Scala, Clojure, and Kotlin Programmers.
Support: The Kit has got great community support. In case, if you wish to resolve a bug or just want a new feature added then it could easily be requested on Github. One can even join the community chat on Gitter. The support that gitter provides is for the following libraries: DLJ4, ND4J, DataVec, Arbiter etc. The question regarding the library can also be followed up on StackOverFlow.
About: Core ML was released by Apple. The library is quite new and brings the capability to integrate Machine Learning in your app. The greater part about this library is that you don’t require a deep knowledge of machine learning or neural networks in order to get started. With Core ML integrating machine learning has become so simple.
- Integrates a variety of Machine Learning Models in your app.
- A total of 30 layer types to support deep learning.
- Support of standard models like tree ensemble, SVMs, and generalized linear models.
- Made over low-level technologies such as Metal and Accelerate.
- Takes full advantage of CPU or GPU to maximum performance and efficiency.
- It is very easy to implement.
Support: The Core ML is offered to the developers who are using it with great support. The library has been provided with decent documentation and covers all the topic that might help you with development. The developer can get help for development as well as app development tools. The bug submission, connecting with apple developer, and much more are all too easy. There are also a variety of support articles that can help you with the development.
About: This framework has been developed for Deep Learning. The framework was developed at the University of California, Berkeley. This one is an open-source library. The library was written in C++ and it can be implemented for Python(which is itself primarily known for machine learning). The framework is being used in different areas such as academic research projects and various startup projects. In fact, the framework is also being used in many big large-scale industrial applications. The framework is being used for Vision, Speech, as well as Multimedia. The version that we have mentioned also supports Recurrent Neural Networks.
- The framework supports a variety of Deep Learning architectures.
- Can easily be used for image classification and image segmentation.
- Support for CNN, RCNN, and LSTM along with fully connected neural networks design.
- Also supports CPU based acceleration with computational kernel libraries like NVIDIA cuDNN and Intel MKL.
Support: If the support of the framework is concerned then one can easily find answers on the Github page. There one can find a variety of answers to the queries that you may be having. There is also a dedicated page known as Facebook Research that provides proper detail about the framework. In case if you wish to reach the page then click on the link here.
About: This is another Machine learning framework which is completely open-source. The libraries for this one has been developed over on top of the art open source stacks. This will enable the developers to manage and deploy services that are ready for production and comes up with predictive capabilities for business solutions. It basically comprises of two things: PredictionIO Framework and Event Server.
As a framework, it provides learning stack for building, evaluating, and also the engine to deploy that machine learning framework in your app architecture. This one also provides you with multiple layers of machine learning analytics. This one uses Apache Spark for processing and with it, one can use HBase and JDBC as the backend.
- It offers the developer a variety of customizable templates.
- It gets you real-time responses with queries.
- Come with data unification.
- The modelling of machine learning and integration can be easily done.
- A variety of machine learning and data processing libraries.
Support: It has a great community that is both warm and welcoming. The community makes sure that the new budding developers are capable of handling the framework without any issues. It is also very easy to connect with them. All you have to do is subscribe to the service. To get support for enterprise solutions, you might have to directly mail them.
Google ML Kit
About: Google has always been there to solve crisis using advanced tech. Google ML Kit is one such utility that can be used by various developers to create apps with Machine Learning. The Kit is capable of analysis a plethora of material such as Video and Audio. It can also be used to label images, detect barcodes, text, faces, and objects.
It also used Natural language processing that can identify up to 58 languages. The library is also capable of providing suggestions along with replies. With this kit, the developer can build their own ideas and deploy them with ease.
- The library has been optimised for mobile development.
- It has been made with Google Expertise.
- Offers different types of integration such as Cloud, Custom, or anything depending upon your choice.
- Optimised for mobile app development.
- Fully supports out of the box ideas with cloud and custom models.
Support: The Google ML Kit comes with ample support for any new budding developer. They shell out new articles about the new features and updates that constantly happen on their blog. Also, Google ML Kit has a Facebook, Twitter, Medium, and Youtube page. This simply means that none of the questions is going to be left unheard.
So these were some of the best AI Engines that can be used to create apps for mobile platforms. These kits will help you churn out great AI-based projects and with cost to create an app since all of them are open source. We hope this article would have been of some help to you. Also, thank you for reading it until the end.
Aman Gaur, writing about Apps and websites and startups at www.agicent.com.