Data Science has recently attracted the attention of academia and industry, particularly in the rapidly changing areas of big data and AI. This makes it very difficult for companies to stay up-to-date and choose the right time to embrace new technologies, i.e. the modern data stack. Unlike universities, which usually experiment with the latest features, companies are often more conservative because of their existing software landscape.
Academic leader:
Jonathan Fürst, ZHAW Zurich University of Applied Sciences, jonathan.fuerst@zhaw.ch
Industry leader:
Pavlin Mavrodiev, pavlin.mavrodiev@ubs.com
The goal of this working group is to provide a technology radar and expertise for software at the intersection of Big Data and AI, with a focus on end-to-end enterprise architecture. The topics include but are not limited to:
- Scalable Big Data architectures as enablers of AI
- Data science/AI/ML platforms
- AI-based data management
- Modern data analytics pipelines
- Scalable AI for intelligent exploration of structured and unstructured data (relational and non-relational data)
Interesting reads:
- Big Data & AI: https://www.unifiedinfotech.net/blog/big-data-and-ai-landscape/ (accessed Dec. 2, 2020)
- Resilience and Vibrancy: The 2020 Data & AI Landscape:
https://mattturck.com/data2020/ (accessed Dec. 2, 2020)