A company is implementing a new network architecture and needs to consider the requirements and considerations for training and inference. Which of the following statements is true about training…

Question

Options

• ATraining architecture and inference architecture have the same requirements and considerations.
• BTraining architecture is only concerned with hardware requirements, while inference architecture
• CTraining architecture is focused on optimizing performance while inference architecture is focused
• DTraining architecture and inference architecture cannot be the same.

• A
  10% (3)
• B
  3% (1)
• C
  87% (27)

Explanation

Training architectures are designed to maximize computational throughput and accelerate model convergence, often by leveraging distributed systems with multiple GPUs or specialized accelerators to process large datasets efficiently. This focus on performance ensures that models can be trained quickly and effectively. In contrast, inference architectures prioritize minimizing response latency to deliver real-time or near-real-time predictions, frequently employing techniques such as model optimization (e.g., pruning, quantization), batching strategies, and deployment on edge devices or optimized servers. These differing priorities mean that while there may be some overlap, the architectures are tailored to their specific goals--performance for training and low latency for inference.

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