Best 20 Machine Learning Service Providers For Cloud Computing

Machine learning is a part of artificial intelligence in computer services. Cloud computing big data is processed through machine learning for the application. To process that big data there is some ML service provider around the world. They take your data, design as per your requirement, and gives the solution as per your instruction.

Machine Learning Service Providers

Machine Learning Service Providers For Cloud ComputingML is the data analysis process to automate the analytical model. It is a sub-sector of artificial intelligence to learn from data, identify the pattern, and discuss human interference.

ML is the art of getting computer performance without explicitly programming language. The self-driving car, practical speech recognition, and effective web search are the best contributions to machine learning.

1. Tensorflow

Tensorflow is the brainchild of Google’s brain team.  It is an open-source machine learning software library for dataflow programming. This is also used in the neural network. It is used for research and production of google.

TensorFlow was released on November 9, 2015, under Apache 2.0 open source license.TensorFlow is the second generation of google’s brain system. The first version was released in February 2017.

TensorFlow is available on Mac OS, Windows, mobile computing platforms, including Android and iOS, 64-bit Linux. It follows the data flow graph to do the complex numerical task. TensorFlow provides deep support in ML, deep learning, IoT, cloud computing, and flexible numerical computation, along with many scientific domains. It is easy to coordinate with cloud computing architecture.

2. Caffe Machine Learning

Caffe machine learning is a deep learning framework. Originally it was developed at UC Berkeley in C++ language with Python interface. Caffe machine learning is open source, under a BSD license.

It supports different deep learning frameworks for image segmentation and image classification. Caffe supports LSTM, CNN, RCNN, and completely connected neural network designs. This machine learning framework is used in academic research projects.

Yahoo has integrated Caffe, and Facebook announced to use the coffee machine learning process. Caffe machine learning is a deep learning framework with speed, expression, and modularity in mind. The choice to change between CPU and GPU by setting a lone flag to train on a GPU machine, then deploy to mobile devices or commodity clusters.

3. Amazon Machine learning

Amazon Machine learning is the product of Amazon. It is popularly known as AML. Amazon Machine learning is a collection of tools and wizards for sophisticated, intelligent, high-end, and learning models.

Amazon Machine learning can connect to the data stored in Amazon S3, RDS, or Redshift. This AML carries out binary sorting, regression, or multi-class classification to produce new models. This machine learning works without actually tinkering with the code.

The technology behindhand Amazon Machine learning is used by Amazon’s inside data scientists. The purpose is to power their AWS Cloud Services, which is highly dynamic, scalable, and flexible. AML also supports the IoT framework.

4. Apache Singa

Apache Singa is distributed deep learning which uses the model of parallelizing and partitioning the training process. This is a robust and straightforward programming model based on cluster nodes.

The main function of Apache Singa is natural language and image recognition. Singa was developed based on a deep learning model. It can run on asynchronous and synchronous, or even hybrid training methods. Singa has three components like IO, Model, and Core.

IO performs reading and writing data to the network and disk. The core component handles memory management functions and tensor operations. Data structures and Model houses algorithms used for ML models.

5. Microsoft CNTK

Microsoft CNTK is the open-source ML framework of Microsoft. CNTK is popular for its speech recognition arena. It is also popular for image training. Microsoft CNTK supports a wide variety of machine learning algorithms like RNN, LSTM, Sequence-to-Sequence, Feed Forward, and CNN. It is one of the dynamic machine learning frameworks of the world.

6. Torch

The torch is the simplest ML framework. It is going fast and easily, especially for Ubuntu users. The torch was developed in 2002 at NYU. It is widely used in big technologies company like Facebook and Twitter.

Torch uses an uncommon but easy language called Lua. It is a responsive programming language with beneficial error messages, a huge repository of example code, guides, and an accommodating community.

7. Accord.NET

Accord.NET is also an open-source ML framework. It is based on the .NET framework and perfect for scientific computing. Accord.NET contains different libraries for applications like statistical data processing, linear algebra, pattern recognition, artificial neural networks, image processing, etc. The libraries of this framework are available as NuGet packages, installers, and source code.

8. Apache Mahout

Apache Mahout is free and open-source software by the Apache Software Foundation. It was built with the purpose of free distributed or scalable ML frameworks. This ML is workable for collaborative filtering, clustering, and classification. This is another easy ML platform. Learn the beautiful topic IoT platform.

9. Theano

Theano started its journey in 2007 at the University of Montreal. This university is popular for world-renown for its algorithms. It is a low-end ML framework but flexible and blazing fast. Theano has a problem which is the error message. The message is infamous for its unhelpful and cryptic nature. However, it is excellent for a research task.

10. Brainstorm

Brainstorm is a straightforward machine learning framework. It worked with neural networks. Brainstorm is written entirely in the Python language. It has smooth and multiple backend systems.

11. BigMl

MLaaS service provider allows data imports from all possible options fromGoogle Drive, Dropbox,  AWS, MS Azure, Google Storage, and more.

12. Alteryx

Alteryx is a machine-learning platform based in Irvine, California. Since 2017, it is a public limited company. Alteryx is an easy and suitable ML platform for the user.

13. is the organization of Mountain View, California. They offer an open-source machine-learning platform. It is easy for developers to use.


KNIME is a Switzerland-based ML platform. It offers a fully open-source Analytics Platform used by over 100,000 people worldwide.

15. RapidMiner

RapidMiner is a Boston, Massachusetts-based organization. It is available as both a free edition and a commercial edition.

16. SAS

SAS is a North Carolina-based organization. It offers numerous software products for analytics and data science. SAS is an industry leader ML platform.

17. MathWorks

MathWorks is a Natick-based privately held company. MATLAB and Simulink are their famous products.

18. TIBCO Software

TIBCO Software is a California-based organization. In June 2017, it entered the data science and machine-learning market.

19. Visionaries

Visionaries are classically minor vendors or fresher entrants representative of trends.  They are usually not familiar with the industry, and therefore often have a little drive relative to Challengers and Leaders ML Platform.

20. Leaders

Leaders have a robust presence and significant minds. It is a cost-effective ML platform.

Final Thought

Every ML service provider is different. Everybody has self-character, pricing, and language. You can choose any of the Machine learning.

Hawlader's passion for technology has driven him to be an avid writer for over 16 years. His vast knowledge of the Windows and Android operating systems is a testament to his proficiency in the field. In addition to his expertise in open source software, he also possesses an extensive understanding of the open-source platform, making him a valuable resource for technology enthusiasts. His contributions to FossGuru writers with research-based articles have helped readers to stay up-to-date with the latest trends in the tech industry. Furthermore, Hawlader's curiosity for scientific breakthroughs has led him to be a keen reader of science blogs, keeping him informed about the latest developments in the field.

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