IBM today announced the launch of the new Deep Learning as a Service (DLaaS) program for AI developers.
With Deep Learning as a Service (DLaaS), developers will be able to train neural networks using popular frameworks such as TensorFlow, PyTorch and Caffe without buying and maintaining costly hardware.
The service allows data scientists to train their models by using only the resources they need, paying only for GPU usage.
Each data processing unit in the cloud has been created for easy use and is ready for programming deep learning networks without the need for infrastructure management by users. In accordance with white paper published by IBM researchers:
Users can choose a set of supported deep learning frameworks, a neural network model, training data, and cost constraints. The service will then take care of the rest, providing them with an interactive, repetitive AI training experience.
For users to use the services they simply have to prepare their data, upload them, to start learning. Then they can download the learning outcomes of their application.
It looks pretty simple and could possibly save weeks programming, according to TNW.
IBM is reportedly working to cope with the difficulty in practicing neural networks or at least to reduce development time. According to a blog post published by the company:
This deep learning as a service is an experimental learning environment which means that users do not have to worry about programming and problem management. The entire lifecycle of learning is automatically managed and results can be viewed in real time and reviewed later. Every educational start is automatically started, monitored and stopped after it is completed, saving time and money for users as they can only pay for the resources they use.
The new Deep Learning as a Service (DLaaS) service works with Watson's excellent platform. This means that it was tested on one of the most advanced AI systems on the planet.
For more information, you can see it IBM blog.
George is still wondering what he is doing here….
