Co-hosted by SAP and WiBD, Palo Alto, CA
In the age of Big Data, data enthusiasts struggle to bring various types of data, such as graph, text, image, streaming, and relational together, and run machine learning algorithms across them. Data scientists are looking for a language-agnostic platform that will allow them to experiment and deploy without extra effort, on-prem, in the cloud, or both. The bigger problem is this: How can we elastically extend or reduce the computation and storage power as needed without sabotaging the work done? Kubernetes technology makes this possible.
For our July 31, 2019 workshop in Palo Alto, our goal was to share our industry experience on how to address some of these obstacles. We presented hands-on exercises on Kubernetes technology, while introducing the SAP Big Data solution for Data Scientists: SAP Data Intelligence.
Women in Big Data (WiBD) members Stella Mashkevitch, Sr. Software Consultant, and Tina Tang, Sr. Director at SAP, kicked off the session with an introduction about WiBD. Puntis Palazzolo, Sr. Data Scientist & Product Manager at SAP, and Gaetan Saulnier, Product Manager at SAP, led the morning technical session on SAP Data Intelligence functionality and architecture , followed by a hands-on workshop in the afternoon.
SAP Data Intelligence is a comprehensive cloud service whereby enterprise AI meets information management. The service was developed to address the current business challenge of operationalizing data that resides in a distributed, cloud-based environment.
Puntis Palazzolo is a Sr. Data Scientist at SAP, where she manages the SAP Big Data solution, Developer-focused and Machine Learning and Artificial Intelligence topics in her role in SAP Data Hub/SAP Data Intelligence Product Management team. She has more than a decade of experience in software design and development, machine learning systems and database technologies in different industries, such as Bioinformatics, Military and Health Care and applications such as Handwriting and Voice Recognition, Image Processing, Natural Language Processing and Recommendation Engines. Puntis has several research publications in the field of Machine Learning and Data Science and has patented ideas in the field of Recommendation Engines. Her academic background is in Computer and Electrical Engineering, Computer Science and Software Engineering.