NCA-AIIO Question 46
Select 3An AI team at a company is developing a natural language processing (NLP) model that needs to be deployed on a production server. The team wants to optimize the life cycle of AI development and deployment by leveraging NVIDIA software tools. Which software components should they use to streamline this process?
- A
NVIDIA TensorRT for model optimization and inference acceleration.
- B
NVIDIA Triton Inference Server for scalable model deployment.
- C
NVIDIA DIGITS for training and managing models in production.
- D
NVIDIA NGC Catalog for pre-trained models and software containers.
- E
CUDA Toolkit for low-level GPU programming related to model training.
Show answer and explanation
Correct answers: A, B, D
Explanation
To streamline the AI development and deployment life cycle, the team should use NVIDIA TensorRT for optimizing and accelerating inference, NVIDIA Triton Inference Server for deploying models at scale, and NVIDIA NGC Catalog for accessing pre-trained models and ready-to-use software containers. These tools provide significant efficiency and performance gains. While CUDA Toolkit and DIGITS have their use cases, they are not specifically tailored to the AI life cycle or production deployment.
- A. Correct.
NVIDIA TensorRT is designed for optimizing AI models and improving inference performance, making it a crucial component for deployment.
- B. Correct.
NVIDIA Triton Inference Server provides a scalable and flexible solution for deploying AI models in production environments.
- C. Incorrect.
NVIDIA DIGITS is primarily focused on deep learning training workflows but is now largely deprecated and not recommended for production deployment.
- D. Correct.
NVIDIA NGC Catalog offers pre-trained models and software containers that help accelerate development and deployment workflows.
- E. Incorrect.
CUDA Toolkit is primarily used for low-level GPU programming and optimization, but it is not specific to AI life cycle management or deployment.