Large models are in high demand, which brings a massive number of parameters; models like the Switch Transformer from Google
is a case in point. Besides evaluating the cost of AI training, businesses must acquire the appropriate hardware infrastructure and pinpoint which specific use cases and applications to seek with large DL models.With optimized and miniaturized AI models increasingly being deployed to support smart sensors, devices and gateways, businesses should devise an edge AI deployment strategy and explore the benefits of placing AI at the edge .
Partner with solution providers and chipset suppliers embracing heterogenous and open AI infrastructure: Scaling up and out of AI applications will be crucial to maximizing the value of AI because developers need support for various AI model designs, development and deployment across different computing platforms. Therefore, heterogeneous computing platforms will be a strategic benefit for enterprise AI.It’s best to seek solutions from AI infrastructure suppliers with a solid physical and cybersecurity track record.
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