2018 RethinkingMachineLearningDevelo

• (Lai & Suda, 2018) ⇒ Liangzhen Lai, and Naveen Suda. (2018). “Rethinking Machine Learning Development and Deployment for Edge Devices.” arXiv:1806.07846

Subject Headings: Machine Learning Model Deployment System; Machine Learning Model Deployment System; Deep Neural Network; Deep Learning; Artificial Neural Network.

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• http://scholar.google.com/scholar?q=%222018%22+Rethinking+Machine+Learning+Development+and+Deployment+for+Edge+Devices

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Abstract

Machine learning (ML), especially deep learning is made possible by the availability of big data, enormous compute power and, often overlooked, development tools or frameworks. As the algorithms become mature and efficient, more and more ML inference is moving out of datacenters / cloud and deployed on edge devices. This model deployment process can be challenging as the deployment environment and requirements can be substantially different from those during model development. In this paper, we propose a new ML development and deployment approach that is specially designed and optimized for inference-only deployment on edge devices. We build a prototype and demonstrate that this approach can address all the deployment challenges and result in more efficient and high-quality solutions

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2018 RethinkingMachineLearningDevelo	Liangzhen Lai Naveen Suda			Rethinking Machine Learning Development and Deployment for Edge Devices						2018