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title = "Talks"
description = "My talks, presentations and podcasts"
template = "prose.html"

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lang = "en"
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## Practical AI Podcast - Sep 2024

[#288 GraphRAG (beyond the hype)](https://changelog.com/practicalai/288)

> Seems like we are hearing a lot about GraphRAG these days, but there are lots of questions: what is it, is it hype, what is practical? One of our all time favorite podcast friends, Prashanth Rao, joins us to dig into this topic beyond the hype. Prashanth gives us a bit of background and practical use cases for GraphRAG and graph data.

## AICamp San Francisco - Jun 2024

[The role of knowledge graphs in RAG](https://youtu.be/MlxZ87WmopI?si=0RC864qe4IkT0fHg&t=1892)

> In this talk, we will explore some of the ways in which knowledge graphs are used in conjunctions with LLMs and graph databases to power RAG systems that provide insights from structured or unstructured data. We will also highlight some practical methods on how to build RAG systems that utilize the power of graphs.

## FOSDEM 2024 - Lightning Talks - Feb 2024

[Kùzu: A Graph Database Management System for Python Graph Data Science](https://fosdem.org/2024/schedule/event/fosdem-2024-1962-kzu-a-graph-database-management-system-for-python-graph-data-science/)

> This talk presents Kùzu: a new open-sourced graph database management system (GDBMS) that is designed for graph data science (GDS) eco-system, specifically in Python. GDS applications require a series of data processing steps, such as extracting data from tabular sources into a graph of nodes and relationships, cleaning and transforming the graph, extracting node features, and finally moving data into a GDS package, such as NetworkX and PyTorch Geometric for graph analytics. These steps can be performed easily and efficiently by GDBMSs, which provide high-level graph-based data models and query languages to developers. Kùzu is a GDBMS designed to serve as an essential storage system for GDS developers.

> Kùzu's embedded architecture makes it very easy to import as a library without a server setup and also provides performance advantages. Specifically users can: (i) ingest and model their application records in various raw file formats, such as Parquet or in-memory Pandas DataFrames, as a graph; (ii) query and transform these graphs using Cypher query language; and (iii) export graphs into popular Python GDS packages like NetworkX and PyTorch Geometric with no copy cost.

> The talk is tailored for data scientists and engineers. We will briefly provide the necessary background on graph analytics. We'll briefly walk through code examples showcasing how Kùzu makes developing GDS pipelines easier, via its integrations with the PyData ecosystem.

## Practical AI Podcast - Jul 2023

[#234 Vector databases (beyond the hype) ](https://changelog.com/practicalai/234)

> There’s so much talk (and hype) these days about vector databases. We thought it would be timely and practical to have someone on the show that has been hands on with the various options and actually tried to build applications leveraging vector search. Prashanth Rao is a real practitioner that has spent and huge amount of time exploring the expanding set of vector database offerings. After introducing vector database and giving us a mental model of how they fit in with other datastores, Prashanth digs into the trade offs as related to indices, hosting options, embedding vs. query optimization, and more.

## Engineer your Career (EYC) Podcast - Jun 2020

[#12 Prashanth Rao – Data Science from Mechanical Engineering](https://www.eycpodcast.com/podcast/episode-12/)

> In this episode, we talk to Prashanth Rao a Data Scientist and Software Developer in Vancouver, Canada. In our conversation with Prashanth, we talk about what it was like for him to transition from mechanical engineering to data science as well as hear some great approaches to being a better lifelong learner.
