Joe Krystofik
Head of Product Planning, Analytics Transformation
As Product Planner, Network Automation at Fujitsu Network Communications Inc., where he leads the development of AI / ML-driven networking software solutions. Prior to joining Fujitsu, he was a Network Planner at Verizon Communications where he focused on the technology evolution of the IP/MPLS Core network. Joe holds 13 years of industry experience focused primarily on the adoption of software applications into multi-layer networks.
Joe Krystofik
Empowering Data-Driven Decision Making Artificial intelligence (AI) classification and clustering techniques are generating new insights that allow network operators to unlock the hidden potential of their distributed and disconnected data sets. These two aspects of AI have opened up an expansive range of new possibilities for getting the best performance out of networks. AI classification
Joe Krystofik
Let’s start out by explaining why capabilities enabled by Artificial Intelligence/Machine Learning (AI/ML) and network automation will be—and are becoming—the driving mechanism for evolving 5G network infrastructure. The true “driver” here is actually network monetization, in the form of new or expanded revenue streams. But monetization opportunities for 5G services can only arise if the
Joe Krystofik
In the not-so-distant past, monetizing services in the radio access network (RAN) was limited by archaic and excessively complicated business support systems (BSS) connected to thousands of incompatible systems. Onboarding new services was a lengthy process that involved tedious systems integration initiatives and did not include horizontal expansion and automation into other service layers. Multiply
Joe Krystofik
As more and more 5G services come online, consuming, processing, and taking action with organizational data has outpaced the capabilities and capacity of traditional databases and now demands increasingly sophisticated data visualization tools and applications. Time-series databases and artificial intelligence and machine learning (AI/ML) are needed to build, train, and interpret network models capable of