webinar

Explaining GPUs to Your CEO: The Power of Productization

Discussions about the future of data and artificial intelligence, from the Internet of Things (IoT) to predictive analytics, generally focus on potential to reshape how we live and how products perform for the better for accurately predicting cataclysmic failures or business problems far faster than ever imagined. But all this potential will remain theoretical if enterprises do not have the processing power to analyze all the data at their disposal. When trying to integrate real-time or streaming data into their BI and AI platforms, many organizations are experiencing crashes due to the limits of their current processing capabilities. In other words, the expansion in data must be accompanied by an expansion of the capacity to process it.

To extract timely meaning form data in the future, many companies will have to change, adapt, or move on from their current technologies. GPU databases are one of the best ways for enterprises to get full utility from streaming data in real time and to converge big data analytics with machine learning AI workloads in a single platform.

Join the CTO and Editor of CITO Research and Forbes contributor Dan Woods for an illuminating webinar about how GPUs can handle the quantity, speed, and diversity of data, and merge these disparate streams into a single database. This results in huge performance boosts for companies and gives them the ability to integrate BI and AI.

By attending, you will be able to explain to your CEO:

  • The difference between CPUs and GPUs for enterprise computing
  • How GPUs and the software that leverages them help you get the most out of your data and how they can augment your existing data supply chains
  • Reasons why your CEO should consider adding GPUs to his or her organization, including AI and machine learning capabilities, increased processing speed, and the ability to stream data and handle multiple data sources
    GPU use cases, such as location-based analytics, BI acceleration, operationalizing machine learning, and real-time IoT analytics