Video

4:00 - 4:36 is really describing output/UAT type testing -- something that is crucial!

It'd be interesting to dive into the specifics of how one might test if a model's accuracy goes up on a test dataset or if its error rates go down. The high level stuff matters, but the implementation is not arbitrary either

Very useful!

Good to see you brother. Always on new things and making us inspire. Way to go..

How about using Vertex AI Feature Store? It's based on BigQuery.

16:30 I agree with Nicolas. There is a difference between having historical features and versioned features

please share powerpoint/slide ?

Can we get the slides somewhere?

Great video! The discussion on data privacy regulations and machine learning was insightful. Do you think new privacy laws will hinder the progress of machine learning?

Truth spoken ..,

It was sob obvious you were reading from a script. Why didn't you just get AI to do it? Terrible

That was an extreamly good episode. As somebody that's trying to pivot into this career space as a 45 year old current student. I have come to realize that I do not know enough about what fields exist much less what are viable career prospects compared to the hype. Add to the equation that my degree plan is not going to prepare me or make me hireable in itself it leaves me feeling like a man in a small boat riding huge swells in the dark. Thank you for the content. I do appreciate the effort and the grind. It is time I catch up on your catalog.

Good prediction, considering this is 100% possible now at least from the digital aspect with Vercel AI Generative UI combined with langchain.

Not very relevant to the topic. Yes there were considerations, but mainly how to solve them with Mystic.

such a wonderful and insightful session

Pretty gold tbh. Love how he gets into some very specific ops recommendations.

I appreciate how the speaker was grinning through the entire talk lol. Jokes aside this is a bleeding edge topic and one really not covered anywhere else.

how do I poo poo it lol what does it mean

Nce vedeo

One of about two talks on the webs that actually addresses these topics. Wonderful. Wish it was an hour.

My god she drinks what with what? That woman's hard as nails.

Please study acoustics

I don't see it's something new that is being built in Uber for sure

Really interesting topic, not the best episode. Unlikely most of the time, I don't feel like I learned a lot.

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:) this is great!

Start with why! Timeless advice, indeed

just bought the book, really great summarization of LLMs challenges, thank you for doing this

I saw Linus lees talk on the AI Engineer summit about embeddings, the demo was very cool, I just came across this video as the AI Engineer World fare is going on, this was awesome too.

Fascinating stuff

This is really interesting :) I am currently studying Data Science and wanted to ask with which methods we can grant/deny permission to certain (internal business) data based on the level of the employee, in an LLM-conversational application? Can we use guardrails for this as well? Thank you a lot in advance for your quick answer!

Is that Ryan Gosling ❤

def correct.. there's body language, tone, etc... llm is just too low bandwidth

Demetrios, my man, I loved the format. Now I'm off to read the post 😅

🎉

😮😮

Very very helpful thanks 🙏

may you share link to this data set?

Like for the intro song

Thanks for the overview! Michelangelo seems like quite the feat of engineering. Major kudos to the engineers who designed and built this out. Would be awesome to get more insight into how they calculated the trade-off between costs associated with the long-term development/maintenance/management of a custom system like this versus using something off the shelf and fully managed like Sagemaker/VertexAI/etc. Obviously you are going to be paying a premium for something like Sagemaker, but I can't help but be skeptical that going with a custom approach like this would pay-off in the long term considering the significant engineering effort that must go into ongoing development and refinement, especially when considering the immense complexity of a system like this. Maybe I'm just not thinking big enough haha

This sounds like a proper LLM optimization software. Langchain is just something that is stitched together in a very brittle way... Whereas DSP looks to ground itself on very concrete foundations!

Any related GitHub links to this? And any thoughts on if use of the python vLLM library for paged attention would be able to get to place of 1M requests a day for 15/48$

Loved this!!

Where can I find Linus' previous talk from a year ago? Would you post a link, please? Stop looking. I found it: czcams.com/video/rd-J3hmycQs/video.htmlsi=g6uWd96bxXuqgWXF

Very insightful!!

I think for the text you meant to use a BERT model rather than a CNN based model

Great presentation!

Brilliant talk

Are "question", "answer", "long_document" and "summary" variables, and if so, where are they declared? 5:17 What operation does this symbol "->" perform?