What ML framework does AWS provide for high-level abstraction of deep learning tasks?

AWS provides a high-level abstraction for deep learning tasks through its support for Apache MXNet, particularly with the SageMaker platform. MXNet includes a built-in high-level API called Gluon, which allows developers to create neural networks in a more intuitive and flexible way, focusing on the model's construction without delving deeply into the backend operations.

Gluon facilitates the rapid development of deep learning models, offering pre-built layers and components that can be easily integrated. This approach reduces the complexity associated with lower-level APIs, allowing data scientists and machine learning practitioners to prototype and iterate quickly on their models. Furthermore, MXNet is optimized for performance on AWS infrastructure, taking full advantage of distributed computing capabilities, which is essential for training large-scale machine learning models efficiently.

In contrast, while TensorFlow and PyTorch are also popular frameworks for deep learning, they are not specifically highlighted for use within AWS's high-level abstraction strategy. SciKit Learn, although an excellent tool for traditional machine learning tasks, is not focused on deep learning, making it less relevant when discussing high-level abstractions of deep learning in the context of AWS services.