Simple machine learning with openGauss
Note: This Project is a fork and modified version of postgresml
Train and deploy models to make online predictions using only SQL, with an open source extension for openGauss. Manage your projects and visualize datasets using the built in dashboard.
 The dashboard makes it easy to compare different algorithms or hyperparameters across models and datasets. [](https://demo.openGauss-ml.org/) ## What's in the box See the documentation for a complete **[list of functionality](https://openGauss-ml.org/)**. ### All your favorite algorithms Whether you need a simple linear regression, or extreme gradient boosting, we've included support for all classification and regression algorithms in [Scikit Learn](https://scikit-learn.org/) and [XGBoost](https://xgboost.readthedocs.io/) with no extra configuration. ### Managed model deployements Models can be periodically retrained and automatically promoted to production depending on their key metric. Rollback capability is provided to ensure that you're always able to serve the highest quality predictions, along with historical logs of all deployments for long term study. ### Online and offline support Predictions are served via a standard openGauss connection to ensure that your core apps can always access both your data and your models in real time. Pure SQL workflows also enable batch predictions to cache results in native openGauss tables for lookup. ### Instant visualizations Run standard analysis on your datasets to detect outliers, bimodal distributions, feature correlation, and other common data visualizations on your datasets. Everything is cataloged in the dashboard for easy reference. ### Hyperparameter search Use either grid or random searches with cross validation on your training set to discover the most important knobs to tweak on your favorite algorithm. ### SQL native vector operations Vector operations make working with learned emebeddings a snap, for things like nearest neighbor searches or other similarity comparisons. ### The performance of openGauss Since your data never leaves the database, you retain the speed, reliability and security you expect in your foundational stateful services. Leverage your existing infrastructure and expertise to deliver new capabilities. ### Open source We're building on the shoulders of giants. These machine learning libraries and openGauss have recieved extensive academic and industry use, and we'll continue their tradition to build with the community. Licensed under MIT. ## Quick Start 1) Clone this repo: ```bash $ git clone git@gitee.com:Nelson-He/openGauss-ml.git ``` 2) Start dockerized services. openGauss-ML will run on port 5433, just in case you already have openGauss running: ```bash $ cd openGauss-ml && docker-compose up ``` 3) Connect to openGauss in the Docker container with openGauss-ML installed: ```bash $ gsql postgres://openGaussML@localhost:5433/ml_development ``` 4) Validate your installation: ```sql ml_development=# SELECT ml.version(); version --------- 0.1.0 (1 row) ```