Harriet Yue Huang
I have worked as a data scientist and BI developer for both the public and private sector in Canada. I have built Python applications in my day job including but not limited to: higher-education institutional teaching resource optimization for a major public research university, topic modeling for public opinion survey to facilitate better public policy making, risk detection in disease onset, record linkage to deduplicate fuzzy records in financial databases. I taught AI courses on a part-time basis for a public college post-graduate diploma program to better equip international students for North American analytics job markets. As a volunteer enthusiast to build up a local Python technology community, I also advocate for Python technologies together with AI modeling in founding the Public Data Technology Forum meetup group and running regular workshops since 2014, while running its corresponding YouTube tech talks channel too:
Public Data Townhall: https://www.youtube.com/channel/UCi6e2FiTbDrRdh90sPdCMXQ
Sessions
With increasing adoption of Streamlit to create interactive data applications in the usage of generative AI technologies, a challenge of maintaining responsiveness under heavy or concurrent user interactions has emerged as applications grow in complexity, sometimes with a long-running background job. This is where integrating task queueing systems like Redis Queue (RQ) into Streamlit applications can come in handy.
In this talk, we will explore how we can enable this integration between RQ and Streamlit to achieve concurrency, improve user experiences and effectively manage long-running tasks.