AugLy VS river

Their recent effort to generate training data for spotting stuff that includes unsanctioned narratives comes to mind. https://github.com/facebookresearch/AugLy

Data augmentation is usually helpful: https://github.com/facebookresearch/AugLy

AugLy.

Some of the technologies which could be integrated include differential privacy, distributed online machine learning, misinformation resilience and multi-party computation, all within the context of smart contracts and bioinformatics.

Facebook Blog: https://ai.facebook.com/blog/augly-a-new-data-augmentation-library-to-help-build-more-robust-ai-models/

River is a Python library for online machine learning. Online machine learning can dynamically adapt to new patterns in the data, or when the data itself is generated as a function of time, e.g., stock price prediction, content personalization.

Today, I'm going to take you on a thrilling coding adventure inspired by a LinkedIn code snippet, where I tangled with FastAPI, River, Watchdog, and Tenacity. Ready? Buckle up!

Complimentary: river and skorch

River - https://github.com/online-ml/river (Online machine learning, best used with Bytewax for Kafka integration)

Hey, great work. Do you think this algorithm would be amenable to be done online? I'm the author of River (https://riverml.xyz) where we're looking for good online clustering algorithms.

If you don't know Rust, but know Python, you can install Python libraries written in Rust with pip. Like, pip install polars or pip install robyn. In this case you follow the two bottom links. But then you don't write your own libraries and stuff so.. I guess that's not what you want.

But, if you want to learn Rust, you probably wouldn't start out with pyo3. You first install Rust with https://rustup.rs/ and then check out the official book, and the book rust by example, that you can find here https://www.rust-lang.org/learn - and maybe write some code on the Rust playground https://play.rust-lang.org/ - then, you use pyo3 to build Python libraries in Rust, and then use maturin https://www.maturin.rs/ to build and publish them to Pypi.

But if you still prefer to begin with Rust by writing Python libraries (it's a valid strategy if you are very comfortable with working with multiple stacks), the Maturin link has a tutorial that setups a program that is half written in python, half written in Rust, https://www.maturin.rs/tutorial.html (well the pyo3 link I sent also has one too. You should refer to the documentation of both, because you will use the two together)

After learning Rust, the next step is looking for libraries that you could leverage to make Python programs ultra fast. Here https://github.com/rayon-rs/rayon is an obvious choice, see some examples from the Rust cookbook https://rust-lang-nursery.github.io/rust-cookbook/concurrenc... - when you create a parallel iterator, it will distribute the processing to many threads (by default, one per core). The rust cookbook, by the way, is a nice reference to see the most used crates (Rust libraries) in the Rust ecosystem.

Anyway there are some posts about pyo3 on the web, like this blog post https://boring-guy.sh/posts/river-rust/ (note: it uses an outdated version of pyo3, and doesn't seem to use maturin which is a newer tool). This post was written by the developers of https://github.com/online-ml/river - another Python library written in Rust

Oh they definitely can be incrementally updated, there is just added complexity. Online learning has been used with more classical machine learning methods in real-time analytics for a while now. River is a library that handles that.

We have both bandits and FTRL implemented in River (https://riverml.xyz) if that helps.