Enabling Edge Native Analytics at The Age of IoT
Goals and objectives
Until recently, data scientists designed algorithms based on the assumption that the data being analyzed would be moved to a single, centralized repository, such as a data lake or a cloud data center. However, amid of the exponential growth of data along with the rise of Internet of Things (IoT), social media, mobility and other new sources of data, the business value of this data can only be harvested through advanced analytics, such as modern artificial intelligence (AI) in the form of machine learning and deep learning. However, these datasets not only keep growing at an accelerated pace, they are fundamentally and inherently distributed, scattered across geographical regions that span the globe, imposing severe constraints on how data can be analyzed in near real-time, and in a cost-effective, or even in a cost-viable manner.
In this session we will discuss the rise of Edge computing and how to be ready to address the IoT market with digital transformation.