Artificial Intelligence (AI) is in its early stages of development, but in the next 15 years, it will touch nearly every business and consumer operation. One of the central features of AI development is the various software frameworks needed to make it possible. Framework refers to a collection of libraries, interfaces, and tools created for generating AI models, such as Neutral Networks (NNs), which enable Deep Learning (DL). A significant rationalization of frameworks is beginning; however, it is unlikely that a de-facto framework will emerge, given the many AI use cases one framework would need to cater to.
To gain more insight on AI technology development, ABI Research, a market-foresight advisory firm providing strategic guidance on the most compelling technologies, has identified and benchmarked the key software frameworks likely to form the backbone of any AI product development.
Although TensorFlow currently leads the pack, several other frameworks are emerging as potential contenders likely to shape specific use cases and applications. These challenger frameworks currently lack the size and scale of developer community interaction but are undertaking intense rationalization around interoperability, in a significant move against the dominance of TensorFlow. “This is positive for the AI technology ecosystem. No one framework is likely to serve every AI use case and segment of the supply chain well. A multiplicity of interoperable frameworks will enable developers to better research and productize AI,” says Jack Vernon, Industry Analyst at ABI Research.
The current market for AI technology has been so far shaped by proprietary technologies. This has led to a huge fragmentation of the AI applications development and has created confusion for developers and implementers of AI technology. As it has been the case with many industries before, including mobile devices ecosystems, desktop operating systems, and internet browsers, technology rationalization is a key milestone of any technology development. AI will not be an exception.
Frameworks are beginning to see rationalization around two factors. “First, a few frameworks have either died off in terms of developer and community support (such as Theano) or have been updated to accommodate a greater breadth of deep learning techniques (such as Torch moving to Pytorch and Caffe moving to Caffe2). Second, there has been a number of frameworks governing bodies choosing to cooperate around a series of standards that will enable deep learning models to be exchanged between them. For example, the Open Neural Network Exchange will rebalance the framework ecosystem which is disproportionately dependent on the success of TensorFlow and its commercial backer Google,” Vernon explains.
ABI Research has assessed the landscape of AI Frameworks and developed a benchmark measuring several KPIs such as developer interest, GIthub commits, scalability, edge accessibility, hardware portability, hardware efficiency, productization, governance, future proofing, and reliability. It is the first report of its kind to access the full state of each framework, and the benchmarking scores found that TensorFlow followed by Caffe2 as the clear leaders, followed by MXNet. These frameworks have repeatedly displayed a commitment to open standards, and their respective commercial backers have shown a dedication to educating their respective developer communities.
These findings are from ABI Research’s Benchmarking AI Frameworks: Productization, Market Rationalization, and Ecosystem Development report. This report is part of the company’s AI & Machine Learning research service, which includes research, data, and Executive Foresights.
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About ABI Research
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