Artificial intelligence is taking over many sectors in technology in the last few years. Developers from all different backgrounds finally realized the opportunities AI an achieve for them regardless of their needs. And as usual in any new buzz, proprietary solutions are always developed to try to take a piece of the new market, but open source ones were also developed to allow everybody to have their share of the new technology.

In today’s article, we list 7 of the best open source AI/Machine learning systems.

Open Source AI/Machine Learning Systems & Frameworks

TensorFlow

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TensorFlow is probably the most known open source framework for machine learning in the world. It is developed by Google, and offers APIs for with many programming languages such as Python, Javascript, C++ and Java.

It also supports mobile operating systems (iOS and android, for iOS it supports Swift programming language), so you can use it to build a mobile app that utilizes machine learning on-the-fly to build models and train them based on real world data.

TensorFlow has many other features, for example it offers TensorFlow Extended (TFX) which helps you in deploying production, scalable and high-performance pipelines anywhere, and TFDV to help you validate data at scale, and TensorFlow Model Analysis to visualize and analyze the machine learning models that you have built. It’s indeed a complete framework, but you know what’s better? It’s all open source and free!

TensorFlow is written in the Python programming language, that’s why you can easily install it on Windows, macOS and Linux distributions with no time. It’s also integrated into Google Cloud, so you can deploy it directly to production if you want.

For more information and installation details, head to the official site.

Scikit-learn

Scikit-learn is a machine learning framework written entirely in Python. It allows you to run classification, regression, clustering and dimensionality reduction processes on your data using the models you have built, supporting both supervised and unsupervised learning methods.

Scikit-learn is a Python-only implementation, so despite it not being that much portable against the Java/Javascript frameworks, it still can be used on all modern desktop operating systems.

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What makes Scikit-learn special is the huge, high-quality documentation it offers, along with the consistent API across all its parts. Scikit-learn is also very clear in what estimator you may need to use when dealing with it:

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Image via scikit-learn.org

For more information, visit Scikit-learn’s homepage.

Torch

Torch is a complete scientific computing environment written for LuaJIT, which is a Just-in-time (JIT) compiler for Lua language. Torch is not just a machine learning framework/library, but instead, is a much bigger scientific computing environment, but one of the features it provides is its support for machine learning.

If you are going to use Torch, then you must know that you are definitely going to use one of the huge community-driven libraries and addons that literally cover everything; From machine learning, to parallel processing and passing by visualization libraries… Everything you need in a scientific environment exists in Torch.

What’s sad about Torch is that it has went out of active development 5 months ago and entered the maintenance mode. So despite it being functional and all, you shouldn’t expect any new updates to come any time soon.

For more information, visit Torch’s homepage.

PyTorch

Based on the previous Torch library, PyTorch is a Python-first machine learning framework that is utilized heavily towards deep learning. It supports CUDA technology (From NVIDIA) to fully use the the power of the dedicated GPUs in training, analyzing and validating neural networks models.

PyTorch is very widely used, and is under active development and support. As indeed it should be, because it offers a huge valuable list of features despite it being fully free and open source; It supports distributed training (Training the models using various peer-to-peer computers), has a C++ frontend (Meaning that you can use PyTorch in C++ applications and systems), supports integration into a lot of cloud partners such as AWS, Google Cloud and Microsoft Azure, and has a large community of developers and scientists behind that keep providing it with modules and 3rd-party community addons.

It also offers a huge set of learning resources, from online courses to full API documentations and quick guidebooks, and passing by online forums and Slack channel support… You can always find help in the PyTorch community.

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Learn more about PyTorch from its official homepage.

Microsoft Cognitive Toolkit

Another deep learning library is the Microsoft Cognitive Toolkit. It can be used with Python, C# and C++ languages, and it works on the 64-bit versions only of both Windows and Linux distributions. It’s licensed under the MIT license.

CNTK supports NVIDIA’S CUDA technology, just like Torch and PyTorch. It’s also compatible with the .NET standard, so it can be used to write cross-platform applications with the .NET framework (even on Linux). And it supports the ONNX format (Which is an open source format for neural networks).

For more information about CNTK, you may visit its official homepage.

Accord.NET

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Image via Accord.NET

This framework, as you should’ve already realized from its name, is mainly built for the .NET framework. It’s more than just a machine learning framework, instead, it provides statistics, computer vision and image processing methods for anything developed in .NET. Because it of that, it works on Windows, macOS, Linux, android and iOS.

Accord.NET has an advantage over many other frameworks mentioned in this list, which is that it has a built-in support for voice recognition, facial recognition and image-recognition, all in real time. So if you really learn the framework from all its corners, you can use it for any type of tasks you want, and for any type of applications.

A large set of academic publications has been made by using Accord.NET, and there’s a large community of users behind it.

Learn more about Accord.NET from its official website.

DatumBox

Our last item in the list is a framework written entirely in Java. DatumBox, as its developers describe it is:

The Datumbox Machine Learning Framework is an open-source framework written in Java which allows the rapid development Machine Learning and Statistical applications. The main focus of the framework is to include a large number of machine learning algorithms & statistical methods and to be able to handle large sized datasets.
Datumbox Devs.

The developers of DatumBox provide an online premium API which utilizes the DatumBox Machine Learning Framework to do various prebuilt advanced tasks. If you do not wish to use that, then you can simply download the machine learning framework, build your models and train them yourself.

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Learn more about DatumBox from its official website.

Conclusion

So you have seen in this post how many great open source machine learning models exist, and they are very good in terms of quality and functionalities they provide. It would be very hard to say that using a propertary machine learning/AI framework is a must.

If you have any other honorable mentions to add to this list, then we would love to hear about them in the comments.

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