An organization is building advanced machine learning models in Google Cloud by using TensorFlow. They want to develop their models faster with purpose-built hardware. Which solution should the organi

The correct answer is A. TPUs. TPUs (Tensor Processing Units) are Google's custom-designed ASICs (Application-Specific Integrated Circuits) purpose-built to accelerate TensorFlow machine learning workloads. They deliver significantly higher performance and efficiency for training and inference of large ML mode

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Question

An organization is building advanced machine learning models in Google Cloud by using TensorFlow. They want to develop their models faster with purpose-built hardware. Which solution should the organization use?

Options

ATPUs
BCPUs
CCPUs
DGPUs

How the community answered

(34 responses)

A
88% (30)
B
3% (1)
C
3% (1)
D
6% (2)

Explanation

TPUs (Tensor Processing Units) are Google's custom-designed ASICs (Application-Specific Integrated Circuits) purpose-built to accelerate TensorFlow machine learning workloads. They deliver significantly higher performance and efficiency for training and inference of large ML models compared to general-purpose hardware. CPUs are general-purpose processors suitable for a wide range of tasks but are not optimized for ML matrix operations. GPUs offer parallel processing useful for ML but are not purpose-built for TensorFlow the way TPUs are. Note: the original question lists 'CPUs' for both choices B and C, which appears to be a typo-one was likely intended to be 'GPUs.'

Topics

#Machine Learning#TensorFlow#TPUs#Hardware Acceleration

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