A data science team is evaluating foundation models for a code generation task. They need a model that is fine-tuned for code and can be deployed on Amazon SageMaker. Which THREE criteria are important to consider when selecting a model?
Trap 1: The training algorithm used
Incorrect. The training algorithm used is not a primary selection criterion; it is more relevant during model development and fine-tuning, not directly for deployment selection.
Trap 2: Model size and architecture
Incorrect. While model size and architecture influence performance and cost, they are secondary considerations after licensing, cost per token, and context window are evaluated.
- A
Licensing and usage terms
Correct. Licensing and usage terms determine whether the model can be legally used for commercial code generation, with models like Code Llama having specific licenses that may restrict fine-tuning or redistribution.
- B
Cost per token for inference
Correct. Cost per token for inference is a critical operational factor, as it directly impacts the budget for deploying the model on SageMaker, especially for high-volume code generation.
- C
Context window length
Correct. Context window length is essential for code generation, as it limits the size of code snippets or files that can be input, affecting the model's practical utility.
- D
The training algorithm used
Why wrong: Incorrect. The training algorithm used is not a primary selection criterion; it is more relevant during model development and fine-tuning, not directly for deployment selection.
- E
Model size and architecture
Why wrong: Incorrect. While model size and architecture influence performance and cost, they are secondary considerations after licensing, cost per token, and context window are evaluated.