This task focuses on design considerations when using foundation models in applications. The key factors to consider when selecting pre-trained models include cost, modality, latency, multilingual support, model size, complexity, customization, and input/output length.
1. Cost Considerations
Balancing accuracy with cost is crucial. For example, choosing between a highly accurate, expensive model or a less accurate, cheaper one depends on your specific requirements. It’s important to find a cost-effective solution that doesn’t sacrifice performance.
2. Latency and Inference Speed
Some AI applications require real-time processing, so models with slow inference times may not be suitable. For example, a self-driving vehicle system needs fast decision-making, which may rule out models like KNN. KNN performs a lot of its work during inference, making it slower for real-time decisions.
3. Modalities
Different modalities (e.g., text, image, audio) require specific models or embeddings. Ensemble methods (using multiple models) may provide better results than a single model, and for multilingual applications, selecting a model trained in the necessary languages is essential.
4. Architecture and Complexity
The architecture and complexity of the model also matter. CNNs are great for image recognition, while RNNs are suited for natural language processing. Complex models generally offer higher accuracy but need more computational power.
5. Model Evaluation
When evaluating models, consider the relevant performance metrics like accuracy, precision, recall, and mean average precision. Different tasks may prioritize different metrics, so it’s important to choose the most relevant ones for your task.
