NeuNetS: Automating neural network model synthesis for broader adoption of AI

phys.org | 12/19/2018 | Staff
Mijac (Posted by) Level 3
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On December 14, 2018, IBM released NeuNetS, a fundamentally new capability that addresses the skills gap for development of latest AI models for a wide range of business domains. NeuNetS uses AI to automatically synthesize deep neural network models faster and easier than ever before, scaling up the adoption of AI by companies and SMEs. By fully automating AI model development and deployment, NeuNetS allows non-expert users to build neural networks for specific tasks and datasets in a fraction of the time it takes today—without sacrificing accuracy.

AI is changing the way businesses work and innovate. Artificial neural networks are arguably the most powerful tool currently available to data scientists. However, while only a small proportion of data scientists have the skills and experience needed to create a high-performance neural network from scratch, at the same time the demand far exceeds the supply. As a result, most enterprises struggle to quickly and effectively get to a new neural network that is architecturally custom-designed to meet the needs of their particular applications, even at the proof-of-concept stage. Thus, technologies that bridge this skills gap by automatically designing the architecture of neural networks for a given data set are increasingly gaining importance. The NeuNetS engine brings AI into this pipeline to fast-track results. Using AI for the development of AI models brings a new and much-needed degree of scalability to the development of AI technologies.

NeuNetS - Environment - IBM - Cloud - Kubernetes

NeuNetS runs on a fully containerized environment deployed on the IBM Cloud with Kubernetes. The architecture is designed to minimize human interaction, automate user workload, and improve over usage. Users do not need to write code or have experience with existing deep learning frameworks: Everything is automated, from the dataset ingestion and pre-processing, to the architecture search training and model deployment. As the field of automating AI is moving...
(Excerpt) Read more at: phys.org
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