explorer VS Phoenix

The Explorer library [0] in Elixir uses Polars underneath it.

[0] https://github.com/elixir-explorer/explorer

To ensure you do not miss this: LiveBook comes with a Vega Lite integration (https://livebook.dev/integrations -> https://livebook.dev/integrations/vega-lite/), which means you get access to a lot of visualisations out of the box, should you need that (https://vega.github.io/vega-lite/).

In the same "standing on giant's shoulders" stance, you can use Explorer (see example LiveBook at https://github.com/elixir-explorer/explorer/blob/main/notebo...), which leverages Polars (https://www.pola.rs), a very fast DataFrame library and now a company (https://www.pola.rs/posts/company-announcement/) with 4M$ seed.

Already exists. Check out https://github.com/elixir-nx/explorer which provides a tidyverse-like API in Elixir using polars as the back end.

José from the Livebook team. I don't think I can make a pitch because I have limited Python/R experience to use as reference.

My suggestion is for you to give it a try for a day or two and see what you think. I am pretty sure you will find weak spots and I would be very happy to hear any feedback you may have. You can find my email on my GitHub profile (same username).

In general we have grown a lot since the Numerical Elixir effort started two years ago. Here are the main building blocks:

* Nx (https://github.com/elixir-nx/nx/tree/main/nx#readme): equivalent to Numpy, deeply inspired by JAX. Runs on both CPU and GPU via Google XLA (also used by JAX/Tensorflow) and supports tensor serving out of the box

* Axon (https://github.com/elixir-nx/axon): Nx-powered neural networks

* Bumblebee (https://github.com/elixir-nx/bumblebee): Equivalent to HuggingFace Transformers. We have implemented several models and that's what powers the Machine Learning integration in Livebook (see the announcement for more info: https://news.livebook.dev/announcing-bumblebee-gpt2-stable-d...)

* Explorer (https://github.com/elixir-nx/explorer): Series and DataFrames, as per this thread.

* Scholar (https://github.com/elixir-nx/scholar): Nx-based traditional Machine Learning. This one is the most recent effort of them all. We are treading the same path as scikit-learn but quite early on. However, because we are built on Nx, everything is derivable, GPU-ready, distributable, etc.

Regarding visualization, we have "smart cells" for VegaLite and MapLibre, similar to how we did "Data Transformations" in the video above. They help you get started with your visualizations and you can jump deep into the code if necessary.

I hope this helps!

There's a package in the Nx ecosystem called Explorer (https://github.com/elixir-nx/explorer). It uses bindings for the rust library, polars, which is much more betterer than Pandas.

FWIW around April this year I started using erlport with python polars in a production ETL app because explorer didn't have the features I needed at the time.

Machine learning is rapidly expanding within the Elixir ecosystem, with tools such as Nx, Axon, and Explorer being used both by individuals and companies such as Amplified, as mentioned above.

A standard Phoenix app contains a priv/repo/seeds.exs script file, which populates a database when it is run, so that developers can work with a conveniently prepared environment.

There was one in the Phoenix Framework (Elixir) about issuing certificates with an invalid end date: https://github.com/phoenixframework/phoenix/issues/5737

Interestingly, Azure had this bug some years ago too leading to an outage. https://azure.microsoft.com/en-us/blog/summary-of-windows-az...

Official website: https://www.phoenixframework.org/

Since you mention Rails, have you seen https://www.phoenixframework.org/

Thus, we set out to build a desktop application using a LiveView from the Phoenix Framework in Elixir. For the uninitiated, a LiveView is a process that receives events, updates its state, and renders updates to a page as diffs. The LiveView programming model is declarative: instead of saying “once event X happens, change Y on the page”, events in LiveView are regular messages which may cause changes to its state.

It seems though like Phoenix is similar like Blazor Server (using web socket), but Phoenix is: SEO friendly (first render is plain html) Light weight, scales well and concurrency is first class Easy to develop (runs a local server so you see live updates) Compiled With auth out of the box https://www.phoenixframework.org/

Sorry to hear this. Phoenix v1.7 changed how it structures files in disk and that broke quite some of the getting started material. However, the guides are always kept up to date, so you can give it a try: https://hexdocs.pm/phoenix/overview.html

You can also see the resources on this page listed by year: https://github.com/phoenixframework/phoenix/blob/main/guides... - the recent launched ones are most likely up to date.

Yes! I love Elixir :) [Phoenix LiveView](https://www.phoenixframework.org/) is really amazing. I feel so fast working in it. I got hooked after watching Chris McCord's ['Build a real-time Twitter clone in 15 minutes'](https://www.youtube.com/watch?v=MZvmYaFkNJI&embeds_referring...), and things have improved a lot since then.

I still work on a lot of Java projects. As of JDK 17 Java has most of "ML the good parts" and has the same scalable, reliable and high-performance threading Java is famous for. JAX-RS provides a Sinatra style framework that makes it easy to write JSON API back ends. JDK 21 is just about to come out as a long term supported version and it will be even better.

I do my side projects in Python with aiohttp and think it is a lot of fun even though people tell me it is suicide (I guess if you block the thread you are in trouble)

I think "Next.js" really wants a node.js backend which has the big advantage that you can share code with the front end and back end. It's basically single-threaded but I know people who are happy with it.

The system I'd most like to try is

https://www.phoenixframework.org/

which is just great if you want to do stuff with websockets that is more interactive than what most people are doing.