Which of these frameworks is specifically designed for AI model training and inference?

MindSpore is specifically designed for AI model training and inference, making it the correct choice. It is a deep learning framework developed by Huawei that focuses on ease of use, performance, and efficient deployment of AI models. Its architecture supports distributed training and is optimized for various computing scenarios, including cloud and edge environments.

MindSpore provides a comprehensive API that allows for the building, training, and deployment of models in a streamlined manner, enhancing productivity for machine learning practitioners. It also offers features like automatic differentiation and model optimization, which are crucial for effective AI development.

In contrast, the other options serve different purposes: TensorBoard is primarily a visualization tool for monitoring and debugging machine learning experiments, Keras is a high-level neural networks API that works on top of TensorFlow, providing an interface for model building rather than being a standalone framework for training and inference. Meanwhile, Pandas is a data manipulation library used primarily for data analysis and manipulation, rather than for training or deploying AI models.