Enabling Data-driven Culture with J!Quant and Intel Xeon Scalable Processors – Intel on AI – Episode 26
In this Intel on AI podcast episode: Many large enterprises need more accuracy and atomization for planning and purchasing. Yet, when generating and analyzing such a massive volume of data, the algorithms use an enormous amount of memory and processing can be slow, problematic, and often not calculate correctly at all? Dionisio Agourakis, the CEO at J!Quant, joins the Intel on AI podcast to talk about how J!Quant has a diverse portfolio of products involving deep learning (DL) and time-series prediction for stock optimization, demand forecast, and profit forecast to help their customers. He talks about how J!Quant helps enable a data-driven culture within their customers’ decision-making processes in order to stay relevant, profitable and open to new opportunities. Dionisio also discusses a specific use case utilizes the 2nd Generation Intel Xeon Scalable processors to tackle a memory bounded algorithm that a customer had and were able to successfully process the inference using Intel processors and Intel Optimizations for Tensorflow.
To learn more, visit:
jquant.com.br
builders.intel.com/ai/membership/jquant
software.intel.com/en-us/frameworks
Visit Intel AI Builders at:
builders.intel.com/ai
Posted in:
Artificial Intelligence, Audio Podcast, Intel, Intel on AI
