Handicapping the AI modeling horse race

AI has become a core focus for application developers everywhere. It’s as hot in the consumer space as it is in business, industry, research, and government

Handicapping the AI modeling horse race

One key milestone of tech market maturation is when leading alternatives narrow to a two-way horse race. That now describes the market for AI modeling frameworks, which are the environments within which data scientists build and train statistically driven computational graphs.

The AI modeling horse race narrows to TensorFlow vs. PyTorch

The clear leaders in AI modeling framework are now the Google-developed TensorFlow and the Facebook-developed PyTorch, and they’re pulling away from the rest of the market in usage, share, and momentum.

Though TensorFlow still has the predominant market share among working data scientists, PyTorch has come along fast among key user segments. According to this recent study, PyTorch has become the overwhelming favorite of data scientists in academic and other research positions; whereas TensorFlow continues to have strong adoption by enterprise AI, deep learning, and machine learning developers. PyTorch has built its following on such strengths as seamless integration with the Python ecosystem, a better designed API, and better performance for some ad-hoc analyses.

Going forward, most working data scientists will probably use some blend of TensorFlow and PyTorch in most of their work, and these will be available in most commercial data scientist workbenches. The most recent feature refreshes to both frameworks are rather underwhelming, as befits a market in which core functions are well defined and users prize feature parity over strong functional differentiation.

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