I’m an IBM Technical Evangelist, bringing my expertise in Big Data Analytics, Data Science, Open Source Technology, and Machine Learning to help drive the adoption and facilitate the success of IBM’s Cloud, and Analytics platforms.
I am a collaborative professional with more than 20 years of experience architecting, developing, and operationalizing analytics and transactional solutions at scale. I have worked in several verticals including Life Sciences, Healthcare, Financial, and E-Commerce. My expertise spans Big Data Analytics, Data Science, Data Engineering, Data Architecture, Machine Learning, Business Analytics, Project Management, and Technical Blogging.
I hold numerous certifications in Data Science, Cloud Computing, and Analytics as well as a Bachelor of Science in Physics from Michigan State University and a Master of Science in Applied Physics from the University of Massachusetts. Most recently, I earned a Master of Science in Strategic Analytics from Brandeis University.
Technical Evangelist - Cloud, Analytics & Watson P
With the advent of IoT, connected devices, and sensors, data is being generated at a phenomenal rate, particularly at the edge of the network. IDC’s FutureScape for IoT report found that by 2019, 40% of IoT data will be stored, processed, analyzed and acted upon at the edge of the network where it is created (1).
Why at the edge? Turns out that sensor data, in most cases has a shelf life. That is, its value is realized within a narrow window after its creation.
This workshop examines current architectural approaches to analytics at the edge, which includes IoT devices, sensors, network communications with edge gateways, and cloud data centers.
You will have a hands-on experience with sensor devices and examine how they collect, process, and analyze data at the edge. E