What is the recommended deployment solution for serving multiple AI models trained in different frameworks in production?

The NVIDIA Triton Inference Server is designed specifically for serving multiple AI models from different frameworks in a production environment. It supports a wide variety of model formats including TensorFlow, PyTorch, ONNX, and others, which allows for deployment flexibility when working with diverse models.

One of Triton's key advantages is its ability to manage and optimize the inference process across different hardware configurations, which enhances performance and scales effectively with demand. Triton also facilitates the integration of model versioning and ensemble models, allowing developers and data scientists to deploy multiple versions of models or combine predictions from different models seamlessly.

In contrast, other options cater to more specialized functions. NVIDIA TensorRT focuses on optimizing individual models for inference acceleration, making it less suitable for managing multiple models from various frameworks simultaneously. NVIDIA Clara Deploy SDK is tailored for deploying healthcare applications and is not a generalized model serving solution. Lastly, NVIDIA DeepOps is meant for orchestration and management of Kubernetes clusters and is not specifically designed for AI model serving.

Thus, Triton's comprehensive features and versatility make it the recommended choice for efficiently serving multiple AI models trained in different frameworks in production.