Dharhas Pothina
Dharhas Pothina is the CTO at Quansight where he helps clients wrangle their data using the pydata stack. He leads the development teams for the Nebari, Conda-Store and Ragna open source projects.
His background includes expertise in computational modeling, big data/high performance computing, visualization and geospatial analysis. Prior to his current position he worked for 15 years in state and federal research labs where he led large multi-disciplinary, multi-agency research projects.
He holds a PhD in Civil Engineering and an MS in Aerospace Engineering from the University of Texas at Austin and a BTech in Aerospace Engineering from the Indian Institute of Technology Madras.
Dharhas is passionate about enabling scientists and engineers with tools that let them scale as well as share their analyses, he loves woodworking, photography and teaching his daughters to love science.
Sessions
While most scientists aren't at the scale of black hole imaging research teams that analyze Petabytes of data every day, you can easily fall into a situation where your laptop doesn't have quite enough power to do the analytics you need.
In this hands-on tutorial, you will learn the fundamentals of analyzing massive datasets with real-world examples on actual powerful machines on a cloud provided by the presenter – starting from how the data is stored and read, to how it is processed and visualized.
Have you experienced the frustration of installing a new Python package, only to discover that your existing code and Jupyter notebooks no longer function as expected? Have you found yourself uttering the phrase, "It works on my computer," to a colleague? Have you ever looked at Randal Munroe's five-year-old XKCD comic on Python environments (https://xkcd.com/1987/) and felt torn between laughter and despair?
Well, there's hope on the horizon. In this presentation, we will delve into an open-source tool that enables the creation and management of a set of stable, reproducible, and version-controlled environments right on your laptop, all through an intuitive graphical user interface (GUI).
As we descend from the peak of the hype cycle around Large Language Models (LLMs), chat-based document interrogation systems have emerged as a high value practical use case. The ability to ask natural language questions and get relevant answers from a large corpus of documents has the potential to fundamentally transform organizations and make institutional knowledge accessible.
Retrieval-augmented generation (RAG) is a technique to make foundational LLMs more powerful and accurate, and a leading way to implement a personal or company-level chat-based document interrogation system. In this talk, we’ll understand RAG by creating a personal chat application. We’ll use a new OSS project called Ragna that provides a friendly Python and REST API, designed for this particular case. We’ll also demonstrate a web application that leverages the REST API built with Panel–a powerful OSS Python application development framework.
By the end of this talk, you will have an understanding of the fundamental components that form a RAG model as well as exposure to open source tools that can help you or your organization explore and build on your own applications.