Global Data Science Forum

Deep Learning Advances from IBM Research 

Thu August 02, 2018 02:48 PM

Earlier this year with contributions made by IBM scientists, IBM introduced Deep Learning as a Service within Watson Studio, a rich set of cloud-based tools for developers and data scientists to help remove the barriers of training deep learning models in the enterprise.

Deep learning and machine learning require expensive hardware and software resources as well as more expensive skilled scientists and developers.  Deep learning, in particular, requires users to be experts at different levels of the stack, from neural network design to new hardware.  Allowing them to be more effective requires cross-stack innovation and software/hardware co-design. The challenges faced in creating AI models and applications have recently been gaining more attention, with Berkeley’s Joe Hellerstein highlighting the AI Engineering Gap,  a new academic conference (SysML) focused solely on the intersection of systems and machine learning, and the Stanford DAWN initiative calling out the “lack of systems and tools for end to end machine learning development.”

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