NCA-AIIO Question 43
Select 3An AI team is working on a deep learning project that involves training a model, optimizing its performance, and deploying it into production. Which of the following software components are essential for managing the life cycle of AI development and deployment?
- A
NVIDIA TensorRT for model optimization and inference acceleration
- B
Kubernetes for orchestrating containerized AI workloads
- C
NVIDIA DeepStream for video analytics and pre-processing
- D
MLflow for tracking experiments and managing machine learning models
- E
PyTorch or TensorFlow for model development and training
Show answer and explanation
Correct answers: A, D, E
Explanation
The AI development and deployment life cycle includes several stages, such as model development, optimization, experimentation, and deployment. NVIDIA TensorRT helps optimize models for deployment, MLflow tracks experiments and manages models, and frameworks like PyTorch or TensorFlow are essential for model creation and training. While other tools like Kubernetes and DeepStream are useful in specific contexts, they are not universally required for managing the entire AI life cycle.
- A. Correct.
NVIDIA TensorRT is critical for optimizing trained models for inference. It plays a significant role in the deployment phase of the AI life cycle.
- B. Incorrect.
Kubernetes is a container orchestration platform, but it is not specific to AI development and deployment life cycle management. While useful, it is not a required component for AI life cycle operations.
- C. Incorrect.
NVIDIA DeepStream is specific to video analytics use cases, which may not be relevant to all stages of the AI life cycle. It is not a general-purpose AI life cycle tool.
- D. Correct.
MLflow is widely used for experiment tracking, model versioning, and deployment management, making it a key component in AI life cycle management.
- E. Correct.
PyTorch and TensorFlow are foundational frameworks for model development and training, which are integral parts of the AI life cycle.