Sklearn-genetic-opt
nyc_traffic_flask
| Sklearn-genetic-opt | nyc_traffic_flask | |
|---|---|---|
| 6 | 2 | |
| 275 | 2 | |
| - | - | |
| 4.6 | 7.4 | |
| 15 days ago | 9 months ago | |
| Python | Python | |
| MIT License | - |
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Sklearn-genetic-opt
- GitHub - rodrigo-arenas/Sklearn-genetic-opt: Hyperparameters tuning and feature selection, using evolutionary algorithms.
- New Python AutoML Package
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Looking for contributors AutoML project in Python
The project is open for collaborators of different levels of expertise, there are some issues about new features, enchacements on docs, etc. Repo: https://github.com/rodrigo-arenas/Sklearn-genetic-opt
- I've been working on an machine learning hyperparameters tuning open source project
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Looking for open source contributors: AutoML
Here is the repo: https://github.com/rodrigo-arenas/Sklearn-genetic-opt
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Introducing Sklearn-genetic-opt: Hyperparameters tuning using evolutionary algorithms [project]
If you want to know more the details or contribute, you can check the Github repository
nyc_traffic_flask
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Where to incorporate MLops into my project?
I have an on/off data science project that uses ML to predict traffic. Lately, I've been wanting to practice more of the Software dev side of data science like DE and MLops. I'm wondering if there's any ways to potentially inject elements of MLops into said project, just for the sake of practice.
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I made a Flask app that predicts traffic in an NYC Street.
https://github.com/benduong2001/nyc_traffic_flask Lemme know if it works.
What are some alternatives?
genetic-algorithm-in-python - A genetic algorithm written in Python for educational purposes.
MAPIE - A scikit-learn-compatible module for estimating prediction intervals.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
superduperdb - 🔮 SuperDuperDB: Bring AI to your database! Build, deploy and manage any AI application directly with your existing data infrastructure, without moving your data. Including streaming inference, scalable model training and vector search.
evalml - EvalML is an AutoML library written in python.
MachineLearningStocks - Using python and scikit-learn to make stock predictions
sklearn-deap - Use evolutionary algorithms instead of gridsearch in scikit-learn
Machine-Learning - Implementation of different ML Algorithms from scratch, written in Python 3.x
de-torch - Minimal PyTorch Library for Differential Evolution
emlearn - Machine Learning inference engine for Microcontrollers and Embedded devices
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
FEDOT - Automated modeling and machine learning framework FEDOT