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Answer: Deploy TFProfiler to assess and optimize the model's performance comprehensively., Utilize TF function to convert the model into a graph for better performance and portability.
The TensorFlow Profiler (E) is the optimal tool for assessing and optimizing the performance of TensorFlow models, especially in scenarios requiring detailed performance analysis and optimization. TF function (A) complements this by aiding in graph creation for performant and portable models, making it a secondary correct choice when optimization and portability are both required. TF Debugger (B) is more suited for debugging purposes, TF Checkpoint (C) for model serialization, and TF Trace (D) for recording operations, none of which directly contribute to performance optimization as effectively as TFProfiler. This approach ensures the model operates efficiently across various production lines while adhering to data privacy standards.
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Your industrial company is implementing a custom deep neural network model developed with TensorFlow to enhance its quality control system. The model is tasked with identifying semi-finished products for discard based on images captured from production lines across various phases. To ensure the model's operational efficiency, you are required to monitor its performance and optimize its inference speed without compromising accuracy. The solution must be scalable across multiple production lines and comply with industry standards for data privacy. Considering these requirements, what is the most suitable solution for this scenario? (Choose two correct options)
A
Utilize TF function to convert the model into a graph for better performance and portability.
B
Implement TF Debugger to log debug information and identify model inefficiencies.
C
Use TF Checkpoint to serialize all model parameters in a SavedModel format for easy deployment.
D
Apply TF Trace to record TensorFlow Python operations in a graph for performance analysis.
E
Deploy TFProfiler to assess and optimize the model's performance comprehensively.