Source: thenewstack.io
Hardware Independence Is Critical to Innovation in Machine Learning
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.
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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