IBM
the system to find hidden insights without being explicitly programmed where to look ,” explains Schnatterly .
There is a lot of exciting work going on in this space , and IBM is credited with having reinvigorated the field of artificial intelligence with the popularity of Watson – IBM ’ s cognitive system that beat Ken Jennings and Brad Netter on the TV game show Jeopardy . But Watson is just one of many offerings that IBM delivers to help clients inject artificial intelligence into applications , business processes , and procedures .
“ We have a software platform called PowerAI , which includes the most popular machine learning frameworks , languages , libraries , tools , and their dependencies , and it is built for easy and rapid deployment . Complementary to PowerAI , IBM also offers a collaboration platform , called Data Science Experience [ DSX ], where folks can come to learn , create , and collaborate about AI and deep learning ,” advises Schnatterly .
DSX supports the complete data science lifecycle , helping data scientists bring their familiar tools such as Jupyter , RStudio , HDP , to curate data and create complex machine learning models and deploy these models into production . “ Hortonworks , who IBM has selected to provide the Hadoop-based data platform , offers DSX to their clients because they see the need and value to marry big data with the complete data science lifecycle ,” Schnatterly adds .
Another recent development is the growing and necessary use of hardware accelerators to mine this vast amount of data and to execute the AI algorithms . “ IBM Power Systems offer unique , and industry leading capabilities , especially in the area of acceleration , that are unlocking new use cases for AI . Together with Nvidia , IBM offers GPU acceleration , but with a unique twist . You see , within the system , the GPU and GPU memory appear as a peer to the CPUs and system memory , with system level speed and bandwidth . Put simply , this means faster access to data , faster machine learning , and better business outcomes ,” says Schnatterly .
“ The need for systems that can handle the demands of AI , larger data
98 October 2017