Jess Nguyen

Jess is the AI software content manager at NVIDIA, expanding the adoption of state-of-the-art tools, libraries, and SDKs that empower data scientists and developers to streamline their daily projects. Her passion is bridging the knowledge gap between complex technologies and their practical applications. Jess has a bachelor of science degree from Cal Poly San Luis Obispo, with a focus on technical communication and electrical engineering.
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Posts by Jess Nguyen

Generative AI / LLMs Feb 27, 2024

Video: Build a RAG-Powered Chatbot in Five Minutes

Retrieval-augmented generation (RAG) is exploding in popularity as a technique for boosting large language model (LLM) application performance. From highly... 2 MIN READ
An illustration representing Apache Spark.
Data Center / Cloud Sep 14, 2023

ICYMI: Run RAPIDS-Accelerated Apache Spark on Amazon EMR

Streamline and accelerate deployment by integrating ETL and ML training into a single Apache Spark script on Amazon EMR. 1 MIN READ
An illustration representing the RAPIDS workflow.
Data Science Aug 23, 2023

ICYMI: Utilizing GPUs for Machine Learning with RAPIDS

Delve into how TMA Solutions is accelerating original ML and AI workflows with RAPIDS. 1 MIN READ
An illustration with 3 different colored squares labeled GPUs in a row.
Data Science Aug 04, 2023

ICYMI: Unlocking the Power of GPU-Accelerated DataFrames in Python

Read this tutorial on how to tap into GPUs by importing cuDF instead of pandas–with only a few code changes. 1 MIN READ
A US map showing different colors representing data visualization.
Data Science Jul 17, 2023

New Video: Visualizing Census Data with RAPIDS cuDF and Plotly Dash

Gathering business insights can be a pain, especially when you're dealing with countless data points.  It’s no secret that GPUs can be a time-saver for... 2 MIN READ
Several graph illustrations representing data science.
Data Science Jun 28, 2023

ICYMI: Exploring Challenges Posed by Biased Datasets Using RAPIDS cuDF

Read about an innovative GPU solution that solves limitations using small biased datasets with RAPIDS cuDF. 1 MIN READ