Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?

An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy.

Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?
A . m5 4xlarge (general purpose)
B . r5.2xlarge (memory optimized)
C . p3.2xlarge (GPU accelerated computing)
D . p3 8xlarge (GPU accelerated computing)

View Answer

Answer: C

Explanation:

Image classification is a machine learning task that involves assigning labels to images based on their content. Image classification can be performed using various algorithms, such as convolutional neural networks (CNNs), which are a type of deep learning model that can learn to extract high-level features from images. To train a customized image classification model, the e-commerce company needs a compute choice that can support the high computational demands of deep learning and provide good accuracy on the model quickly. A GPU accelerated computing instance, such as p3.2xlarge, is a suitable choice for this task, as it can leverage the parallel processing power of GPUs to speed up the training process and reduce the training time. A p3.2xlarge instance has one NVIDIA Tesla V100 GPU, which can provide up to 125 teraflops of mixed-precision performance and 16 GB of GPU memory. A p3.2xlarge instance can also use various deep learning frameworks, such as TensorFlow, PyTorch, MXNet, etc., to build and train the image classification model. A p3.2xlarge instance is also more cost-effective than a p3.8xlarge instance, which has four NVIDIA Tesla V100 GPUs, as the latter may not be necessary for a proof of concept with a small dataset. Therefore, the Machine Learning Specialist should select p3.2xlarge as the compute choice to train and achieve good accuracy on the model quickly.

Reference:

Amazon EC2 P3 Instances – Amazon Web Services

Image Classification – Amazon SageMaker

Convolutional Neural Networks – Amazon SageMaker

Deep Learning AMIs – Amazon Web Services