NCA-AIIO Question 48
Select 3An organization is deploying a machine learning model into production. Their workflow includes data preprocessing, model training on GPUs, version control, deployment on an inference server, and monitoring the deployed model's performance. Which software tools or components are most relevant to this AI lifecycle process?
- A
NVIDIA TensorRT
- B
Git
- C
NVIDIA Triton Inference Server
- D
Kubernetes
- E
NVIDIA CUDA Toolkit
Show answer and explanation
Correct answers: A, B, C
Explanation
The AI lifecycle involves multiple stages, including data preprocessing, model training, deployment, and monitoring. Tools like NVIDIA TensorRT optimize models for inference, NVIDIA Triton Inference Server handles deployment, and Git manages version control, all of which are critical to this process. Kubernetes and the NVIDIA CUDA Toolkit are relevant to infrastructure and acceleration, respectively, but they are not as directly tied to the lifecycle stages as the other components.
- A. Correct.
NVIDIA TensorRT is used for optimizing models for inference, making it a critical component in the deployment phase of the AI lifecycle.
- B. Correct.
Git is essential for version control, allowing teams to manage code and model versioning effectively during development and updates.
- C. Correct.
NVIDIA Triton Inference Server facilitates the deployment and scaling of AI models in production, making it a key part of the lifecycle.
- D. Incorrect.
Kubernetes is a container orchestration tool, but it is not directly specific to the AI lifecycle. It is used for managing deployments at scale but is not directly tied to the AI-specific software components listed here.
- E. Incorrect.
NVIDIA CUDA Toolkit is primarily used for GPU programming and acceleration. While it supports model training, it is a lower-level library and not directly tied to all stages of the AI lifecycle.