On March 20, 2018, IBM launched its brand new Deep Learning as a Service (DLaaS) program for AI developers.
The Deep Learning as a Service, which runs on IBM Watson, is an experiment-centric model training environment. This means users don’t have to worry about getting bogged down with planning and managing training runs themselves. Instead, the entire training life-cycle is managed automatically and the results can be viewed in real-time and can also be revisited later.
The DLaaS service allows data scientists to train models using the resources they need and they simply have to pay only for the GPU time. Users can train their neural networks using a range of deep learning frameworks such as TensorFlow, PyTorch, and Caffe, without the need to buy and maintain the hardware cost.
In order to use the service, users just have to prepare their data, upload it, begin training, then download the training results. This can potentially snip days or weeks off of training times. For instance, if a single GPU setup takes nearly a week to train visual image processing neural network on a couple million pictures, the time taken to do the same thing can be cut down to mere hours with this new cloud solution.
Also, maintaining deep learning systems requires manpower. By investing time in IBM’s DLaaS can scale projects, which can result into clustering just a few GPUs for deep learning models. This experience is an entirely different skill set than training neural networks.
To read more about this in detail, check out IBM’s blog post.