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For a long time, each ML model operated in one data mode – text (translation, language modeling), image (object detection, image classification), or audio (speech recognition). However, natural intelligence is not limited to just a single modality. Humans can read and write text. We can see images and watch videos. We listen to music to relax and watch out for strange noises to detect danger. Bein
[LinkedIn discussion, Twitter thread] Never before in my life had I seen so many smart people working on the same goal: making LLMs better. After talking to many people working in both industry and academia, I noticed the 10 major research directions that emerged. The first two directions, hallucinations and context learning, are probably the most talked about today. I’m the most excited about num
A collection of materials from introductory to advanced. This is roughly the path I’d follow if I were to start my MLOps journey again. Table of contents ML + engineering fundamentals MLOps …. Overview …. Intermediate …. Advanced Career Case studies Bonus ML + engineering fundamentals While it’s tempting to want to get straight to ChatGPT, it’s important to have a good grasp of machine learning, d
[Hacker News discussion, LinkedIn discussion, Twitter thread] A question that I’ve been asked a lot recently is how large language models (LLMs) will change machine learning workflows. After working with several companies who are working with LLM applications and personally going down a rabbit hole building my applications, I realized two things: It’s easy to make something cool with LLMs, but ver
I read every single one of the resumes we receive. Sometimes, I’d talk to a candidate and see that what we perceived as their strongest aspects actually weren’t included in their resume. Occasionally, a candidate would tell me that they didn’t expect their resume to still be screened by humans – had they known, they would have written their resume differently. The resume evaluation process is pret
Note: This note is a work-in-progress, created for the course CS 329S: Machine Learning Systems Design (Stanford, 2022). For the fully developed text, see the book Designing Machine Learning Systems (Chip Huyen, O’Reilly 2022). Slides (much shorter 😁). Original Google Docs version. Let’s start the note with a story I was told by an executive that many readers might be able to relate to. About two
[Twitter discussion, LinkedIn] Updates Jan 3, 2023: Update the online features section to differentiate between real-time features and near real-time features. If you’re interested in this topic, my book Designing Machine Learning Systems (O’Reilly, June 2022) covers online prediction and continual learning in much more detail. Real-time machine learning is the approach of using real-time data to
[Twitter thread, Hacker News discussion] I have a confession to make. I cried during the compiler class in college. I became a machine learning engineer so that I wouldn’t have to worry about compilers. However, as I learned more about bringing ML models into production, the topic of compilers kept coming up. In many use cases, especially when running an ML model on the edge, the model’s success s
Introduction to Machine Learning Interviews Book You can read the web-friendly version of the book here. You can find the source code on GitHub. The Discord to discuss the answers to the questions in the book is here. As a candidate, I’ve interviewed at a dozen big companies and startups. I’ve got offers for machine learning roles at companies including Google, NVIDIA, Snap, Netflix, Primer AI, an
[Twitter thread, Hacker News discussion] Click here to see the new version of this list with an interactive chart (updated December 30, 2020). To better understand the landscape of available tools for machine learning production, I decided to look up every AI/ML tool I could find. The resources I used include: Full stack deep learning LF AI Foundation landscape AI Data Landscape Various lists of t
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