While Amazon SageMaker offers powerful capabilities, there are some challenges:
Data Quality: Ensuring high-quality, well-labeled data is crucial. Poor data can lead to inaccurate models. Model Interpretability: Understanding why a model makes certain predictions can be difficult. Using techniques like SHAP (SHapley Additive exPlanations) can help. Computational Resources: Training large models can be resource-intensive. SageMaker's managed infrastructure helps mitigate this by providing scalable resources.
