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Open source AI is defined as artificial intelligence technology that is publicly available for commercial and non-commercial use under various open source licenses. Open source AI includes datasets, pre-built algorithms, and pre-made interfaces to help you get started with AI application development. 65% of enterprises are expected to start using open source AI technology in the next two years, up from 48% in 2021. This article shortlists the top 10 open source software you need to consider for your next AI project.
Software Providers Directory For Business Intelligence Applications
Open source AI is an artificial intelligence technology that is publicly available for commercial and non-commercial use under various open source licenses. Open source AI can encompass a variety of technologies that are helpful for product teams, independent application developers, and enterprises.
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Open source AI differs from freeware AI applications — the source code is exposed to the user and can be modified and implemented in circumstances other than the original purposes. Open source also comes with a large active community, where developers can contribute and ask for help.
Today, open source artificial intelligence is a thriving segment, benefiting both developers and enterprises. Research Opens a New Window. suggests that while 48% of enterprises currently use open source technologies for AI/ML, this number will increase to 65% by 2023.
As mentioned, AI software can come in many shapes and sizes, encompassing datasets, algorithms and UI, or any combination of the three. In this roundup (arranged alphabetically), we focus on the ten best open source AI algorithm libraries that you can use to build software applications for common AI applications such as computer vision, object recognition, character recognition, speech to Go text to address the situations. , and others.
Disclaimer: This is a curated list based on publicly available information and may include websites that target medium to large enterprises. Readers are advised to do their own final research to ensure they are tailored to their unique organizational needs.
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Overview: Founded in 2019, Accumos is a relatively new entrant in the open source AI software segment – but has the backing of industry leaders AT&T and TechMahindra. Both companies wanted to buck the trend of tech giants like Microsoft, Google and Apple leading open source innovations and making AI available for commercial deployment. So they introduced Acumos AI, a Linux-based design studio to help integrate other frameworks and develop cloud-based AI apps.
USP: The main USP of Acumos AI is its GUI Design Studio feature. It simplifies the development process through visual programming and streamlined AI development, making it more accessible. You can also leverage onboarding tools to enable interoperability with other frameworks like TensorFlow, H2O, etc.
Editorial Comments: Acumos is an attractive open source option for those interested in greater access to AI. It standardizes the infrastructure stack so you can quickly develop and deploy AI applications, and is compatible with most major languages, including Java, Python, and R. The Acumos platform drives AI end-to-end lifecycle of AI/ML applications. Modeling for implementation, development and market release.
Overview: ClearML is the result of the recent rebranding of Allegro AI, which provides open source tools for data scientists and machine learning labs. Along with the rebranding, ClearML announced a free hosting plan to give data scientists the freedom to conduct AI/ML experiments and run workloads without investing in additional resources. ClearML can be leveraged as an MLOps solution, ready to implement with just two lines of code.
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USP: ClearML is one of the few open source AI software that comes with optional commercial add-ons like priority support, well-defined SLAs and managed services. If you want the benefits of open source (no vendor dependency, cross-ecosystem compatibility, etc.) while also having a professional vendor partner, ClearML is a great choice.
Editorial Comments: ClearML has exciting capabilities that are rare in the open source segment. For example, even when you choose ClearML Free, you get 100GB of free storage, 3 collaborative workspaces, low integration development, and support for on-premises deployment. At the other end of the spectrum, there is also a convenience store for higher development. In other words, ClearML perfectly balances the simplicity of open source with the feature set of traditional commercial platforms.
Overview: Founded in 2012, H2O has been at the forefront of open source AI innovation for nearly a decade. The company works with tech giants like NVIDIA, IBM, Intel and Google to drive large-scale AI and ML products. The company was recently accredited by Singapore’s Infocomm Media Development Authority (IMDA), further strengthening its global presence and allowing Singaporean public sector organizations to benefit from H2O.
USP: H2O’s biggest USP is its AI hybrid cloud capability. This means it’s an end-to-end platform that allows you to design, model, manage, develop and consume AI (in collaboration with others) in a centralized environment. Since it’s deployed with Kubernetes, you can also run it on any cloud or on-premises infrastructure.
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Editorial Comments: H2O is great for enterprises just getting started with AI, because you can start with an open source platform that trains on your enterprise data. As more applications are developed, H2O can support your enterprise journey through training, improvement requests, auto-ML and other capabilities.
Overview: Microsoft is an open source voice assistant that you can run in any ecosystem. The company has won several awards over the years and is backed by strategic investments from major companies such as Jaguar Land Rover. Basically, Microsoft uses open source AI technology to power various components of the voice stack. There is a large community of users, developers and translators to continuously improve AI algorithms.
USP: Microsoft’s biggest USP is that it is relatively easy to get started. It offers a private AI-based voice option for commercial deployments like Alexa or Siri, which will make data mining at some level inevitable. That is why it is involved in many public sector and philanthropic initiatives, where data privacy is essential.
Editorial Comments: Unlike most open source AI software, Mycroft is strictly use-case oriented. However, if you need a voice assistant and want to choose open source, Mycroft is one of the most powerful options available.
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Overview: The Open Source Computer Vision Library or OpenCV is a rich library of AI algorithms aimed at addressing real-time computer vision tasks. It was launched in 1999 as part of an Intel research project in the early days of AI development. In 2012, it was taken over by a non-profit foundation, which now manages the community, user support and developer support. In 2020, the OpenCV AI Kit campaign was launched to raise money for new hardware modules.
USP: OpenCV is one of the longest and most battle-tested open source AI software libraries in the industry. Since Intel’s inception, it has evolved from a native C++ computer vision library to a more widely accessible and implementation-ready platform.
Editorial Comments: Companies looking to leverage AI-based computer vision to develop facial recognition systems, augmented reality apps, and the like should consider OpenCV. Its rich algorithm library, together with learning support and complementary hardware, creates a 360-degree solution.
Overview: OpenNN is an open source AI software library for implementing neural networks and ML. Its primary use cases include customer intelligence and industry-specific analytics, including their predictive applications. The company developing and maintaining OpenNN is called Artelnics, known for its cutting-edge AI and big data research. Importantly, OpenNN does not specialize in computer vision or natural language processing like some of the other open source software on this list.
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USP: OpenNN’s most prominent USP is its ability to provide predictive insights. You can leverage this open source AI software to build applications for customer segmentation, early healthcare diagnosis, predictive maintenance for equipment and more.
Editorial Comments: Companies, teams and independent developers looking for a pure play open source AI software library (no commercial bells and whistles) should consider OpenNN – especially for predictive analytics use cases. You can draw from its rich documentation set, which also serves as a helpful tutorial to get started.
Overview: PyTorch improves upon the original Torch framework for ML using the Lua programming language. Facebook’s AI research lab launched PyTorch as a Python-based interface for AI/ML app development under an open source license in 2016. A C++ interface to PyTorch is also available. Today, PyTorch has evolved into a rich ecosystem that gives you all the tools you need to accelerate AI development from research to production.
USP: Python’s biggest USP is probably its ready cloud availability on Alibaba Cloud, Amazon Web Services, Google Cloud Platform and Microsoft Azure. This allows you to quickly download software libraries from the relevant application markets and get started without leaving your existing cloud-based development environment.
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Editorial Comments: PyTorch has the widest range of use cases among the best open source AI software. Not only can you use it for computer vision, but you can also apply PyTorch for audio processing, NLP, language translation, and more.
Overview: Rasa is the most popular open source AI
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