7 Machine Learning Tools for IIoT

Source – edgylabs.com

Companies at the forefront of the machine learning field offer open source libraries of solutions for companies and the average person.

Below is a list of seven open-source platforms that help businesses integrate machine learning into their production process. With these toolkits, businesses, regardless of their size, can get access to the same ML resources developed and used by prestigious companies.

The 7 Machine Learning Tools for IIoT:

1. Amazon Machine Learning:

In 2015, Amazon’s subsidiary AWS (Amazon Web Services) launched Amazon Machine Learning as part of its Cloud-based solutions. AML is a deliberately simplified platform intended for developers of any skill level to walk them through the creation of machine learning predictive models.

2. Google’s TensorFlow:

Google uses TensorFlow toolkit for its own products and services. Since 2015, TensorFlow is an open source software library for deep learning. The updated version, TensorFlow 1.0 is now available, with much faster calculations, more flexibility and features, and support for new languages.

3. Microsoft’s Azure:

The Azure Machine Learning Studio is Microsoft’s Cloud-based platform that allows businesses and organizations to benefit from machine learning solutions that are easy to implement. With AMLS‘s collaborative, drag-and-drop machine learning tools, businesses can easily create, test, deploy and share predictive models.

4. H20:

Used by over 80,000 data scientists and 9,000 organizations around the world, H20.ai is the biggest open source AI platform that enables enterprises to get a “digital brain.” H20 products, such as Deep Water, makes the training and deployment of models easy with automatic tuning and a fast GPU-based system.

5. Caffe:

Built by Berkeley Artificial Intelligence Research (BAIR), Caffe is an open source deep learning framework already used for academic research projects, startup prototypes, and large-scale industrial applications.

The Caffe framework offers easy configuration, the ability to switch between GPU and CPU to train models before deployment. It is also one of the fastest systems.

6. MLlib:

Apache Spark is a general-purpose cluster computing framework that, other than high-level APIs and tools, has an open-source machine learning library called MLlib. When you download Spark, MLlib is included as a module, compatible with all APIs.

7. Torch:

Torch, an open-source ML platform, simplifies and speeds up the process of building algorithms.


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