Windmark is a Python module designed for modeling complex, heterogeneous sequential data, where each observation is effectively a data frame. Utilizing a machine learning pipeline, Windmark automates the process of handling sequential data by leveraging the power of Flytekit for workflow orchestration and larger-than-memory streaming data preprocessing. This makes Windmark an ideal choice for data scientists and machine learning engineers who deal with time-series data, nested structures, or any form of sequential data that otherwise requires advanced preprocessing and modeling techniques.
- Automated Machine Learning Pipeline: Streamline the process of modeling sequential data with an automated pipeline.
- Workflow Orchestration with Flytekit: Utilize Flytekit to manage and scale your machine learning workflows efficiently.
- Efficient Larger-than-Memory Data Preprocessing: Leverage Polars for fast and memory-efficient data preprocessing.
- Support for Complex, Heterogeneous Sequential Data: Tailored to handle diverse sequential data formats effectively.
- Data quality checks and data exploration visualizations
- Support for alternative supervised learning tasks (Regression, Survival)
- Automatically generated report of model performance
- Model deployment pipeline
- replace assertions with proper errors
- fix docstrings
- add docstring linter
- add unit tests
- add logo
For more detailed documentation on Windmark, including advanced configurations, API details, and custom usage scenarios, please refer to the Windmark Documentation.
We welcome contributions to Windmark! If you're interested in helping improve the project, please check out our Contributing Guidelines for more information on how to get started.
Windmark was created to simplify and enhance the process of modeling complex sequential data. We thank the open-source community for the support and contributions that make projects like this possible.
For any questions or support, please open an issue on our GitHub repository, and we'll be happy to help.