When we discuss Machine Learning or Artificial Intelligence, we usually refer to the algorithms that gives a computer system the ability to learn and to reason with percept. ML developers and researchers can use AWS Deep Learning infrastructure and tools to accelerate deep learning in the cloud. AWS Deep Learning EC2 instances can be used in all the aspects of ML and AI. The good part is, whether you require Amazon EC2 GPU or CPU instances, Deep Learning AMIs are provided without any additional charges. The AWS’s pay-as-you-go pricing model will only charge you for the resources that are used in storing and running your applications.

  • Base AMI to set up private deep learning engine repositories or custom builds of deep learning engines, developers can use Base AMI which is available in Amazon Linux and Ubuntu versions
  • Conda-Based AMI is available for developers who require pre-installed pip packages of deep learning frameworks in separate virtual environments. Conda AMI is available in Amazon Linux, Ubuntu, and Windows 2016 versions
  • Apache MXNet, Chainer, Gluon, Horovod, Keras, TensorFlow, and PyTorch are the famous deep learning frameworks that are pre-installed on AWS Deep Learning EC2 instances
  • To Accelerate model training, The AWS Deep Learning AMIs include the latest NVIDIA GPU-acceleration through pre-configured CUDA and cuDNN drivers, as well as the Intel Math Kernel Library (MKL)

To get started with machine learning, AWS provides SageMaker which is a fully managed service through which developers and data scientists can quickly build, train, and deploy machine learning models. 

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