Jesse Steinweg-Woods

Staff ML Engineer @Change

[Lior Alexander @LiorOnAI]

Finally, someone did it. Python for React.

React is the most popular front-end framework used to build interfaces and now all python devs can use it.

This means you can code an ML model, develop a backend and design a front end all in one language.

https://t.co/SqfPBoDAEt

[Paul Couvert @itsPaulAi]

Stop using only ChatGPT.

I don't understand why people don't use Chrome AI extensions.

Here are insane AI Chrome extensions to save hours of work:

[Mike Conover @vagabondjack]

Haystack is such a slick framework for out of the box semantic search w transformer, vector index integration. Joined the @deepset_ai office hours this week, team is really on the ball. Wish we had this when we were building @SkipFlag. cc @peteskomoroch

https://t.co/68TeH9aqdA

[Erik Bernhardsson @bernhardsson]

I just wrote a bit about what I'm working on and why I'm excited about it: https://t.co/sj9tfUsMSC

[Hamel Husain @HamelHusain]

Is it that we need more end to end tools like a plug-in to elastic search that “just works” out the box? Is it infra? Or is the marginal benefit smaller than I think it is?

I know some companies are using it, it’s just not as widespread as I expected it to be by now.

[Erik Bernhardsson @bernhardsson]

A theory I have about product development is that you can totally do gradient descent all the time and build a really good product! The main reason teams that get stuck in a “local maxima” is that they believe that every change has to be a Pareto improvement.

[Xavier (Xavi) Amatriain @xamat]

A lot has been speculated about TikTok's recommendations. This is the first paper I've read by the team, and it has many interesting details: expirable embeddings, parameter server, online training... Good #recsys stuff https://t.co/r8UBCrnZi8

[Eugene Yan @eugeneyan]

RecSys 2022 was a blast! Here's a recap with three of my favorite papers and more than a dozen summaries.

Did I miss any key papers? Please comment below!

#RecSys2022

https://t.co/Brz1vuweX7

[Muhammad Hammad Khan @hammad_khan23]

Interesting work by Amazon search “On the Value of Behavioral Representations for Dense Retrieval" 🧵

https://t.co/XFWHgClqA1

[Sebastian Raschka @rasbt]

Stumbled upon this neat flowchart for choosing text classification methods. I usually eye-balled it, but using a samples/number ratio cut-off seems reasonable. I.e., with a samples/number < 1500 use a bag-of-words model, with a >= 1500, use a seq. model (https://t.co/gbLJEGBOJA)

[Chip Huyen @chipro]

My editors just shared with me the feedback from early reviewers and I'm in tears 😭

With the help of so many people, I worked really hard on this book. I'm grateful that people gave it a chance.

Read the book online: https://t.co/fxph4OYIsf

Pre-order: https://t.co/5RHFYzu7kq

[Shreya Shankar @sh_reya]

I probably should have written this years ago, but here are some MLOps principles I think every ML platform (codebase, data management platform) should have: 1/n

[Alexandre Tamborrino @altamborrino]

I'm very happy to share our work on how we implemented semantic search / dense retrieval for better podcast search at Spotify:
https://t.co/LOsiw6RH65

[François Chollet @fchollet]

Compilation thread of various Keras tips ⬇️⬇️⬇️

[Stefania Olafsdottir @StefaniaBje]

Looking for a weekend listen?

Get inspired by @bernhardsson's path from early Spotify days to scaling data teams.

Tons of actionable tips mixed with inspiring and triggering data stories.

Thanks for joining me, Erik!

Who should I talk to next?

https://t.co/bpREsLHUaV

[Radek Osmulski @radekosmulski]

Curious about recommender models?

Interested in endowing models from other domains with some of their superpowers?

Please join me on a whirlwind tour of 6 recsys architectures!

>> a thread 🧵 <<

[Hamel Husain @HamelHusain]

I had fun giving this talk in @chipro 's ML Systems Class @Stanford about a subject we don't talk about enough:

1. How to evaluate ML Tooling
2. How to spot & deal with 🔥Tool Zealots 🔥

We have recorded the full talk 👇
https://t.co/BPEuepEkOX

Also a 🧵👇

[Dr. Parik Patel, BA, CFA, ACCA Esq. @ParikPatelCFA]

It’s not a loss until you sell

[James Kirk @Jiminy_Kirket]

ML Engineers love to say "matrix factorization for recs? What is this, 2014?" and then spend a few million dollars building something worse

[Jo Kristian Bergum @jobergum]

This is the most impactful blog post on medium ever https://t.co/6jwOqAE8XK