Data Science on AWS (San Francisco, Global)

RSVP Webinar: https://www.eventbrite.com/e/webinarkubeflow-tensorflow-tfx-pytorch-gpu-spark-ml-amazonsagemaker-tickets-45852865154

Talk #0: Introductions and Meetup Announcements
By Chris Fregly and Antje Barth, Principal Developer Advocates, AI and machine learning @ AWS

Talk #1: Getting started with Polars (when Dataframe gets little too fast)
By Suman Debnath, Principal Developer Advocate, Data Engineering @ AWS

Abstract: Polars, the fast-growing open source dataframe library, is rapidly becoming the preferred choice for data scientists and data engineers in Python. With its remarkable performance and intuitive syntax, Polars is poised to challenge the dominance of Pandas in the realm of Python dataframes. While Pandas is widely adopted, it has limitations in terms of performance and syntax. Enter Polars, a powerful in-memory library built with Rust and leveraging Arrow. In this session, we will explore the fundamentals of Polars and discover how it achieves blazing-fast operations with its own lazy approach in an intuitive Python API. It’s time to embrace the potential of Polars and revolutionize your data manipulation experience.

GitHub: https://github.com/pola-rs/polars

Talk #2: Deep dive into Generative AI Parameter Efficient Fine Tuning (PEFT), Prompt Tuning, and Reinforcement Learning with Human Feedback (RLHF) workflows
By Chris Fregly, Principal Solutions Architect, AI and machine learning @ AWS

RSVP Webinar: https://www.eventbrite.com/e/webinarkubeflow-tensorflow-tfx-pytorch-gpu-spark-ml-amazonsagemaker-tickets-45852865154

Zoom link: https://us02web.zoom.us/j/82308186562

Related Links
O'Reilly Book: https://www.amazon.com/dp/1492079391/
Website: https://datascienceonaws.com
Meetup: https://meetup.datascienceonaws.com
GitHub Repo: https://github.com/data-science-on-aws/
YouTube: https://youtube.datascienceonaws.com
Slideshare: https://slideshare.datascienceonaws.com