12-03, 17:30–18:00 (UTC), General Track
Asynchronous programming can be intimidating for many due to its unique syntax, paradigm, and different behavior in environments like IPython and Jupyter notebooks.
But it’s not that complicated—and I'll prove it. In this talk, I will demystify the basics, along with some advanced concepts, from a practical perspective. By the end, you'll be ready to get started and implement significant performance improvements in your network or I/O-bound code.
Attend this talk if you’ve been intimidated by async and await for a while and are ready to change that.
In my experience, data professionals struggle with asynchronous programming yet often need to make many API calls and handle I/O-bound operations in R&D projects. When deploying data science solutions, they frequently create endpoints that perform multiple network calls. Async programming is incredibly useful in these scenarios but is often overlooked due to its intimidating learning curve.
The mission of this talk is to help as many people as possible get started with asynchronous programming in Python.
The talk will cover the fundamentals from a practical perspective, such as implementing async endpoints with FastAPI and building a service to batch-process requests. We will go beyond just async and await to implement multiple API calls and address some common pitfalls and their solutions (such as concurrency limits and blocking compute).
No previous knowledge expected
Ryan is SVP of Technology at Boclips, an ed-tech enabling the use of video in education.
With a PhD in astronomy, Ryan has worked across data-centric roles in various startups—from data science and data engineering to leadership roles. He is now responsible for Data, Engineering and Product at Boclips. Ryan's expertise spans machine learning, natural language processing, data pipelines, and large language models, with a core focus on getting data science delivered.
Ryan frequently shares insights on leadership and data on LinkedIn https://www.linkedin.com/in/ryanvarley/.