IBM wants to open up the deep learning expertise bottleneck

With a new offering, Deep Learning-as-a-Service, IBM aims to help businesses automate key business processes, even if they lack expertise


Specifically, it’s employing one set of AI tools to minimise the amount of compute time on its cloud services you need to buy in order to train another set of AI tools to run your business.

That will also allow IBM’s customers to make the most of another scarce and expensive resource, AI expertise, according to Ruchir Puri, chief architect for IBM Watson and an IBM Fellow.






This fine tuning concerns the choice of “hyper-parameters” used in the AI training process. A deep learning expert will have an instinctive feel for what’s right for a particular task, allowing them to minimise the amount of computing resources needed to develop a model, whereas a beginner might be forced to plod through all possible combinations of parameters in order to find the right one.

Deep Learning-as-a-Service


This tuning process is part of IBM’s latest Watson Studio offering, Deep Learning-as-a-Service, for which the company unveiled pricing on Tuesday.


The Enterprise (v2) plan is the most expensive, with a minimum spend of US$6,000 per month. This includes access to the environment for five authorised users and unlimited viewer collaborators, and 5,000 “capacity unit-hours.” (Additional hours are $0.50 each.)

One capacity unit consists of two virtual CPUs with 8GB of RAM; bigger and smaller virtual server instances are available, with a corresponding variation in their capacity unit cost.




One, the Fabric for Deep Learning (FfDL, or “fiddle”), uses the containerised application management system Kubernetes to simplify the management of computing resources in deep learning frameworks. It can orchestrate TensorFlow, Caffe, Caffe2, PyTorch, and Keras workloads across cloud computing fabrics composed of CPUs and GPUs.

The other, Model Asset eXchange (MAX), is a kind of app store for training models, providing a standardised way for businesses to deploy deep learning models that they, or others, have already built.


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