Senior Director, Applied Artificial Intelligence
As a leader in Applied AI with financial services at RelationalAI, Michelle specializes in machine learning and cloud computing and has 15+ years of experience translating cutting-edge technology to industry. Michelle contributed to the original IBM Watson showcased on Jeopardy and serves on multiple boards. She is also passionate about diversity, STEM education/careers for our minority communities, and is on the Board of Women in Data.
Talks and Events
Accelerate Data Products in Financial Services with a Semantic Layer
In financial services, a common language and data model are essential to not only meet regulatory needs but also to stay competitive by creating more products more quickly and monetizing on massive amounts of accumulated, heterogeneous data. In fact, we see an increasing number of semantic layer and modeling tools such as Legend, Morphir, and others coming into the open source realm and gaining adoption amongst other institutions to try to address this. Historically, however, there are challenges with integrating and executing these semantic layers within an existing data infrastructure ecosystem at scale. This often results in obstacles to adoption and difficulties in transitioning efforts to production.
In this talk, we will provide a specific example of how we use relational semantic layers to solve this challenge through a financial services use case. You’ll learn about semantic layers in financial services and how a relational semantic layer fits in a modern data stack. You’ll also get a technical review of an applied financial services use case involving PURE/Legend, and find out how the business benefits from having a generic model of representation and execution that spans all data sources and types (e.g., semistructured, graph, tabular, etc.). The talk will end with forward-looking thoughts on the industry and a chance for you to ask questions of some of the experts implementing these solutions.
Track: Semantic Layer
- Semantic Layer
- Business Use Cases
- Financial Services