PyData Global 2022

Lightning Talks are short 5-10 minute sessions presented by community members on a variety of interesting topics.

Python web frameworks, like FastAPI, Flask, Quartz, Tornado, and Twisted, are important for writing high-performance web applications and for their contributions to the web ecosystem. However, even they posit some bottlenecks either due to their synchronous nature or due to the usage of python runtime. Most of them don’t have the ability to speed themselves due to their dependence on *SGIs. This is where Robyn comes in. Robyn tries to achieve near-native Rust throughput along with the benefit of writing code in Python. In this talk, we will learn more about Robyn. From what is Robyn to the development in Robyn.

The difficulty of transitioning from research to production is a prevalent issue in the machine learning development life cycle. To work more effectively in production, an ML team may need to modularize and rework their code. Occasionally, depending on whether the application requires offline, online, or streaming predictions, this can even necessitate re-implementing and maintaining feature engineering or model prediction logic in several locations.

The audience will learn about an open source microframework for creating machine learning applications in this session. UnionML, developed by the Flyte team, offers a straightforward, user-friendly interface for specifying the fundamental components of your machine learning application, from dataset curation and sampling to model training and prediction. UnionML automatically generates the procedures you require to fine-tune your models and release them to production in various prediction use cases, such as offline, online, or streaming settings using these building blocks. There will be a live demonstration by taking an end to end machine learning based example written in Python