SlickML🧞 Documentation#
🧠 SlickML🧞 Philosophy#
SlickML is an open-source machine learning library written in Python aimed at accelerating the experimentation time for ML applications with tabular data while maximizing the amount of information can be inferred. Data Scientists’ tasks can often be repetitive such as feature selection, model tuning, or evaluating metrics for classification and regression problems. We strongly believe that a good portion of the tasks based on tabular data can be addressed via gradient boosting and generalized linear models [1]. SlickML provides Data Scientists with a toolbox to quickly prototype solutions for a given problem with minimal code while maximizing the amound of information that can be inferred. Additionally, the prototype solutions can be easily promoted and served in production with our recommended recipes via various model serving frameworks including ZenML, BentoML, and Prefect. More details coming soon 🤞 …
🧑💻🤝 Become a Contributor#
SlickML🧞 is trying to build a thriving open source community where data scientists and machine learning practitioners can come together and contribute their ideas to the project. The details of the development process in are laid out in our Contributing guidelines. We strongly believe that reading and following these guidelines will help us make the contribution process easy and effective for everyone involved 🚀🌙 . Special thanks to all of our amazing contributors 👇
❓ 🆘 📲 Need Help?#
Please join our Slack Channel to interact directly with the core team and our small community. This is a good place to discuss your questions and ideas or in general ask for help 👨👩👧 👫 👨👩👦 .