Visualizer VS m2cgen

You saw how I’m doing the schema and data transformations in Visualizer. My project is open source so if you like looking at code more than reading an article here you go https://github.com/mariusmuntean/Visualizer

Find the backend code here and the frontend code here.

If you just want to see the code, here’s the GitHub link for the backend and here’s the link to the frontend. Btw. this is my third post on using Redis, so make sure to read the others too.

What you’re not getting is a spoon-feeding, instead you’re going to see the relevant parts of Visualizer, a pet project of mine, where I’m using Redis to ingest and query tweets from Twitter’s sample steam.

Check out: https://github.com/BayesWitnesses/m2cgen

Notice that I’m using random values to populate the Sentiment field. You might compute the values for your fields based on other fields or actually use an ML model to perform the transformation. E.g. you could make use of m2cgen to transform trained models to pure python code and load them in **RedisGears **to be executed in a *GearsBuilder *instance. Another option is to pull out the big guns and go straight to RedisAI.

I wish that it was more common for model outputs to be converted the way bayeswitness does with mc2gen https://github.com/BayesWitnesses/m2cgen

And that’s it! All the magic in just two lines of code. I would like to thank the authors of the m2cgen library and encourage you to try it out.

I forgot to update the README. We just replaced RNN with xgboost that has a better f1 and is very quick, as the decision trees are translated to plain rust using m2cgen.

Probably, as many frameworks come with a Rust support (or there are wrappers). Some models, like decision tree, can also be automatically translated to plain Rust (in my company we use m2cgen to translate xgboost models to plain rust code).

These repositories on GitHub are good start I think: https://github.com/BayesWitnesses/m2cgen and https://github.com/vickylance/dart_nn