Edge AI is not On-Prem AI
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Edge AI differs significantly from on-prem AI, primarily due to constraints in compute, memory, and energy resources. While edge AI utilizes lightweight pre-trained models for efficiency, on-prem AI can leverage larger models with more resources for continuous training. Applications of edge AI focus on low latency and cost-effectiveness, contrasting with the needs of on-prem AI for substantial computing power.
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Edge AI is not On-Prem AI
- 1. Edge AI is not On-Prem AI!!! Yes!!!!! There is a huge diffrence.
- 2. Edge AI is compute, memory & energy resource constrained On-Prem AI allows for addition of resource blocks
- 3. Workstations are not edge AI devices You can easily replace or add CPU, memory, GPU etc. In edge AI you can't do so.
- 4. Edge AI depends on lightweight pre- trained ML models for running efficiently
- 5. On-Prem AI use case based ML models can be large enough to do continuous on- device training
- 6. Edge AI is used in applications where low latency, high privacy is required & cost is a consideration
- 7. On-Prem AI is used in places with large computing and high privacy needs are required
- 8. Product Dev process for Edge AI is completly different from On-Prem AI From Hardware, Software Libraries to ML Algos everything is different
- 9. Remember!!! AI on servers is not equal to AI on edge
- 10. Edge AI On- Prem AI Cloud AI Know the difference. Save on product development time.}