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8 Machine Learning Toolkits For Mobile Developers To Make Their Apps More Intelligent

Emerging technologies like artificial intelligence and machine learning are being adopted by almost every organisations in the present scenario. To be in this race, developers have started implementing machine learning models to make advancements in their fields. 

With all these, mobile applications have advanced a lot as compared to a few years back. Now, we have facial recognition, speech recognition, etc. abilities available to mobile applications. Developers are using these technologies thoroughly to keep up the pace with these emerging techs. In this article, we list down 8 machine learning toolkits that a mobile developer must know.

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(The list is in alphabetical order)

1| Apache PredictionIO

Apache PredictionIO is an open-source machine learning server which is built as a source stack for developers as well as data scientists. This tool helps a developer to build and deploy an engine as a web service on production and users can implement their own machine learning models seamlessly. 

Click here to know more.

2| Caffe

Caffe (Convolutional Architecture for Fast Feature Embedding) is an open-source deep learning framework developed by Berkeley AI Research (BAIR). This is a popular computer vision framework which can be used by the developers to work on machine vision tasks, image classifications, etc.

Click here to know more.

3| CoreML

CoreML is a machine learning framework introduced by Apple Inc. With the help of this framework, a developer can integrate machine learning models locally on iOS. It supports frameworks such as vision for image analysis, natural language for natural language processing, speech for converting audio to text and sound analysis for identifying sounds in audio.

Click here to know more.

4| Eclipse Deeplearning4j

Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. This tool is integrated with Hadoop and Apache Spark which helps to bring AI into business environments. It serves as a DIY tool where a Java, Scala and Clojure programmers can easily configure deep neural networks. 

Click here to know more.

5| Google ML Kit

This tool is a machine learning software development kit which is designed solely for mobile developers in order to build various interactive features on Android and iOS. This tool comes with a set of ready-to-use APIs for common mobile use cases such as recognizing text, detecting faces, scanning barcodes, labeling images and recognizing landmarks. A developer just needs to feed the data and the tool will perform the rest.

Click here to know more.

6| H2O

H2O is an open-source machine learning platform written in Java, Python and R programming languages which support various widely used algorithms such as gradient boosted machines, generalized linear models, deep learning and more. A developer can easily deploy models into production with Java (POJO) and binary formats (MOJO). 

Click here to know more.

7| Microsoft Distributed Machine Learning Toolkit (DMLT)

Microsoft Distributed Machine Learning Toolkit (DMTK) is a machine learning toolkit which contains highly scalable, efficient, and flexible machine learning tasks on big data. It includes a DMTK framework which supports unified interface for data parallelization, a hybrid data structure for big model storage, model scheduling for big model training, and automatic pipelining for high training efficiency.

Click here to know more.

8| OpenNN

OpenNN is an open-source neural network library for advanced analytics to solve real-world problems. It has developed a software tool known as Neural Designer which allows a developer to build neural network models without the need for programming. 

Click here to know more.

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Ambika Choudhury
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.

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