Which framework is used for deploying machine learning models in a cloud environment?

TensorFlow Serving is the correct choice for deploying machine learning models in a cloud environment due to its design specifically tailored for productionizing machine learning models. It offers a flexible architecture and is optimized for serving TensorFlow models, allowing for easy integration into cloud environments. The framework supports versioning, making it easy to update models without downtime, and it provides tools to manage model loading, monitoring, and health-checking, which are critical for maintaining robust production systems.

While Pandas, PyTorch Lightning, and Numpy are essential tools in the data science and machine learning ecosystem, they serve different purposes. Pandas is primarily used for data manipulation and analysis, PyTorch Lightning is a framework that simplifies training of PyTorch models, and Numpy is a fundamental library for numerical computing in Python. They do not have the specific capabilities or design principles that facilitate the deployment of machine learning models in a cloud context like TensorFlow Serving does.