Chip Huyen LinkedIn Posts

Excited to show what our team Voltron Data has been working on over the last 2.5 years: Theseus, our GPU-native query engine! This

I’m often asked what problem I’d solve if I were to start another company. I probably won’t do a startup any time soon

It’s been so inspiring to hear about the amazing things people are working on in the AI & compute space at #GTC2024!

- Christian

I'm happy to share that I've joined Voltron Data as their VP of AI & Open source software. I'm excited about Voltron's vision

In conversations with companies, I notice a few questions that keep coming up about model evaluation.

1. How much data do we need for

A hard part of building AI applications is choosing which model to use. What if we don’t have to? What if we can

I never dreamed of this happening, but I walked into a public library in Tokyo and found a translated copy of my book!!

Many

Very proud of our team for their work on ibis streaming which is released this week!

GitHub repo: https://lnkd.in/eMcBZQvu

ibis’ core idea is to

I’m making a list of things to consider when using open source models and commercial models. Here’s what I have currently. What else

I have this hypothesis that the most popular enterprise AI applications today aren’t the ones that solve the most important problems or make

I’m excited to share that I’m working on a new book about building applications with foundation models. AI Engineering builds upon Machine Learning

Absolutely loved the discussions and the energy at MLOps Learners’ RAG workshop this week. We had over 200+ comments/questions during the 90 minute

I have this hypothesis that the most popular enterprise AI applications today aren’t the ones that solve the most important problems or make

I’m excited to share that I’m working on a new book about building applications with foundation models. AI Engineering builds upon Machine Learning

A big issue I see with AI systems is that people aren't spending enough time evaluating their evaluation pipeline.

1. Most teams use

LinkedIn has published one of the best reports I’ve read on deploying LLM applications: what worked and what didn’t.

1. Structured outputs
They chose YAML

The last few years saw the maturation of a core component of the MLOps stack: feature platforms. After studying many of these platforms,

The resume evaluation process is pretty much a black box for most candidates. Few hiring managers have publicly discussed this. I thought I

ML models' performance depends heavily on the freshness of data, yet most companies are still stuck in the slow batch workflow.

We (Claypot AI)

The rapid adoption of GPUs had made GPU optimization one of the most sought-after engineering skills. I'm excited for the GPU optimization workshop