AWS Certified Machine Learning Specialty · Free Practice Question Medium
Question 4
A botanical research organization is developing a machine learning solution to identify various species of flowers from images for a biodiversity study. They have already implemented a convolutional neural network (CNN) model capable of recognizing the presence of flowers in images. However, the organization now requires the model to not only detect flowers but also accurately classify them into specific species to aid in their research efforts. Considering the need to efficiently utilize computational resources and minimize model training time, which approach should the organization take to enhance their image recognition model's capability to classify specific flower species?
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A
Employ Amazon SageMaker's built-in image classification algorithm with hyperparameter optimization to retrain the model from scratch for species classification.
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B
Integrate the existing CNN model with Amazon Lex to enhance its natural language processing capabilities for improved species classification accuracy.
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C
Use AWS DeepLens to deploy the current CNN model directly, incorporating a real-time feedback loop for continuous learning and species identification.
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D
Implement transfer learning on the existing CNN model using Amazon SageMaker to fine-tune it for specific flower species classification.
Reveal correct answer
Correct answer: D
Explanation
Consider which AWS services specialize in machine learning model enhancement and how they relate to image classification, not real-time feedback or NLP.
Correct Choice: Implement transfer learning on the existing CNN model using Amazon SageMaker to fine-tune it for specific flower species classification.
Transfer learning in Amazon SageMaker allows for the reuse of a pre-existing CNN model, leveraging its learned features while training it further on a dataset of specific flower species. This method is both efficient and cost-effective, as it reduces the need for training a model from scratch. This approach optimizes computational resources and minimizes training time, making it an ideal solution for extending the capabilities of the existing model to classify flower species accurately.
Incorrect Choice: Use AWS DeepLens to deploy the current CNN model directly, incorporating a real-time feedback loop for continuous learning and species identification.
AWS DeepLens is a deep learning-enabled video camera designed for developers to experiment with machine learning models. While it supports deploying models for real-time image processing, it serves better in scenarios where real-time video analysis is required, not necessarily for enhancing detailed image classification capabilities.
Incorrect Choice: Employ Amazon SageMaker's built-in image classification algorithm with hyperparameter optimization to retrain the model from scratch for species classification.
Amazon SageMaker's image classification algorithm is powerful for building models from scratch, including hyperparameter optimization. However, retraining from scratch for species classification when you already have a base model might not be as efficient as adapting the existing model to recognize new classes, which is where transfer learning shines.
Incorrect Choice: Integrate the existing CNN model with Amazon Lex to enhance its natural language processing capabilities for improved species classification accuracy.
Amazon Lex provides capabilities for building conversational interfaces with natural language understanding (NLU) and automatic speech recognition (ASR). It's not designed for image classification tasks and wouldn't contribute to the accuracy of a model aimed at classifying flower species in images.
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