Assignment 5.4 Selecting Pre-Trained AI Models: Balancing Accuracy, Speed, Size, and Explainability

This infographic presents a comparison of pre-trained AI models across different domains, including natural language processing, computer vision, and tabular data. It highlights key factors such as model size, accuracy, speed, and explainability, which are critical when selecting a model for real-world applications. Models like GPT-3 and BERT demonstrate strong language capabilities, while MobileNetV3 and EfficientNet-B0 show how vision models balance efficiency and performance. TabPFN represents a growing approach to handling structured data with minimal training.

The visual emphasizes that there is no single best model for all use cases. Instead, effective model selection requires balancing trade-offs based on the specific needs of the task. Larger models may offer higher accuracy and flexibility but often come with higher costs and lower explainability. In contrast, smaller and more efficient models provide faster performance and easier deployment. This comparison helps guide informed decisions by aligning model capabilities with practical constraints and application goals.