Hardware Independence Is Critical to Innovation in Machine Learning

2 years ago thenewstack.io

Summary: This is a summary of an article originally published by The New Stack. Read the full original article here →

We’re in the midst of a storm. On one side we have a global chip shortage with no end in sight, https://www.reuters.com/business/autos-transportation/toyota-reports-25-drop-q2-profit-misses-estimates-2022-11-01/.

Achieving hardware independence will enable faster innovation, unlock hybrid options for model deployment and ultimately save practitioners time and energy.

Hybrid Deployment: Hardware independence enables ML models to migrate between or even be split between on-premise and cloud-to-edge.

Enabling fluid migration of ML models between different hardware will enable new experiences and the mode impact of ML on applications.

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