m2cgen
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies (by BayesWitnesses)
go-featureprocessing
🔥 Fast, simple sklearn-like feature processing for Go (by nikolaydubina)
Our great sponsors
| m2cgen | go-featureprocessing | |
|---|---|---|
| 8 | 6 | |
| 2,707 | 113 | |
| 0.6% | - | |
| 0.0 | 5.2 | |
| 6 months ago | about 1 month ago | |
| Python | Go | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
m2cgen
Posts with mentions or reviews of m2cgen.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-02.
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How to use python ML script in tauri?
Check out: https://github.com/BayesWitnesses/m2cgen
- EleutherAI announces it has become a non-profit
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Redis as a Database — Data Migration With RedisOM, RedisGears and Redlock
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.
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Why isn’t Go used in AI/ML?
I wish that it was more common for model outputs to be converted the way bayeswitness does with mc2gen https://github.com/BayesWitnesses/m2cgen
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Use your decision tree model in your Javascript project today with 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.
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We use Rust for an opensource malware detection engine. It's great at detecting ransomwares and we want to share results and ideas with you.
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.
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Is data science/engineering in Rust practical, does it provide any benefit over Python, and what are the best crates?
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).
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Flutter Machine Learning App
These repositories on GitHub are good start I think: https://github.com/BayesWitnesses/m2cgen and https://github.com/vickylance/dart_nn
go-featureprocessing
Posts with mentions or reviews of go-featureprocessing.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-04-05.
-
Machine learning with GOlang
Tabular data is well supported in Go. Tabular data and small sample sizes is a sweet spot for Go. You may squeze in some meaningful performance and good looking code and integrations. Some entry points: (1) https://github.com/nikolaydubina/go-ml-benchmarks (2) https://github.com/dmitryikh/leaves (3) https://github.com/nikolaydubina/go-featureprocessing
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Show HN: Go-Featureprocessing v1.0.0
What is this?
Fast feature preprocessing in Go with feature parity to sklearn
https://github.com/nikolaydubina/go-featureprocessing
What is new?
* Added batch processing
- Feature Processing in Go
- [P] fast and convenient feature processing in Go! I am sure many backend teams are running Go services, if so this should help integration better!
- Fast and convenient library for feature processing in pure Go! I am focusing on single sample processing time, benchmarks and ease of use. Try it out!
What are some alternatives?
When comparing m2cgen and go-featureprocessing you can also consider the following projects:
TensorFlow.NET - .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
bayesian - Naive Bayesian Classification for Golang.
Synapses - A group of neural-network libraries for functional and mainstream languages
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
R Provider - Access R packages from F#
ocrserver - A simple OCR API server, seriously easy to be deployed by Docker, on Heroku as well
gorse - Gorse open source recommender system engine
gago - :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)
randomforest - Random Forest implementation in golang
sklearn - bits of sklearn ported to Go #golang
goml - On-line Machine Learning in Go (and so much more)
m2cgen vs TensorFlow.NET
go-featureprocessing vs bayesian
m2cgen vs Synapses
go-featureprocessing vs gosseract
m2cgen vs R Provider
go-featureprocessing vs ocrserver
m2cgen vs gorse
go-featureprocessing vs gago
m2cgen vs randomforest
go-featureprocessing vs sklearn
m2cgen vs gago
go-featureprocessing vs goml