Top 5+ Best Machine Learning Toolkits for Mobile Developers

Top 5+ Best Machine Learning Toolkits for Mobile Developers

Futuring technologies like artificial intelligence, machine learning, data science, etc are being adopted by approximately all the companies in the present scenario. To stand against the upcoming competition, developers have started implementing machine learning models.

With all of these, mobile applications have moved a lot. Now, we have face recognition, speech recognition, etc. that are available for mobile applications. Developers are using these techniques as an emerging technology. Today we will discuss top 5+ machine learning toolkits which are very important for all mobile developers.

1. Google ML Kit

This tool is a machine learning software development suite which is designed mainly for mobile developers to create various interactive features only on Android and iOS. This tool comes with a set of ready-to-use APIs for common mobile use cases such as text recognition, face detection, scanning barcodes, labeling images and identifying locations.

2. Distributed Machine Learning Toolkit by Microsoft

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

3. CoreML

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

4. 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 create neural network models without any coding.

5. Caffe

Caffe (Comprehensive Architecture for Fast Feature Embedding) is an open-source deep learning framework introduced by Berkeley AI Research (BAIR). This is a popular computer vision framework that can be used for a machine-visual task, image classification, and more.

6. H2O

H2O is an open-source machine learning platform written in Java, Python and R programming languages. It supports various widely used algorithms like Normalized Linear Models, Intensive Teaching, Gradient Boosted Machine, etc. A developer can easily deploy models in production with Java (POJO) and binary formats (MOJO).

Is there any toolkit missing? If yes, then please comment it. Don't feel shy to give your review. Free feel to share. For more updates and AI, ML, and Data Science stay tuned with us at LunaticAI.

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