From: aidotengineer
When choosing and implementing AI models, engineers consider various factors that influence the model’s suitability and performance. A survey of 100 AI Engineers revealed the top considerations in this process [00:09:41].
Key Considerations for Choosing an AI Model
According to a survey of 100 AI engineers, the top considerations when choosing a model include:
- Cost The most frequently cited consideration is the cost associated with the model [00:10:47].
- Latency The speed at which the model processes information and responds is a significant factor [00:11:04].
- Accuracy/Performance Evaluating benchmark scores and overall accuracy are crucial for model selection [00:11:16]. This can also be broadly categorized as “capabilities” [00:13:18].
- Deployment Environment Considerations include whether the model will be served on-premises or off-premises [00:12:02].
- Open Source vs. Closed Source The licensing and accessibility of the model’s code are important factors [00:12:45].
Trends in AI Model Deployment
Future trends suggest a shift in how AI models are deployed and used:
- On-Device Deployment It is predicted that the majority of deployed models will operate directly on devices within approximately 18 months [00:03:34].
- Smaller, Specialized Models There is an anticipated trend towards smaller models that are specifically hyperspecialized for particular tasks [00:03:40]. These smaller models may be orchestrated together to perform more complex functions, moving away from sole reliance on larger, general-purpose models that require sending data elsewhere [00:03:42].
AI Model Training
It is suggested that out of approximately five people currently training large models today, at least one will no longer be involved in training AI models by the end of the year [00:01:55]. This highlights potential volatility or shifts in the field of AI model training.