WAX-ML: A Python library for machine learning and feedback loops on streaming data
This provides tools for researchers and practitioners working with time-series and streaming data, though it is incremental as it builds on existing libraries like JAX.
The authors introduced WAX-ML, a Python library for designing machine learning algorithms and feedback loops on streaming data, which integrates with JAX, pandas, xarray, and Gym to facilitate implementation and use for end-users.
Wax is what you put on a surfboard to avoid slipping. It is an essential tool to go surfing... We introduce WAX-ML a research-oriented Python library providing tools to design powerful machine learning algorithms and feedback loops working on streaming data. It strives to complement JAX with tools dedicated to time series. WAX-ML makes JAX-based programs easy to use for end-users working with pandas and xarray for data manipulation. It provides a simple mechanism for implementing feedback loops, allows the implementation of online learning and reinforcement learning algorithms with functions, and makes them easy to integrate by end-users working with the object-oriented reinforcement learning framework from the Gym library. It is released with an Apache open-source license on GitHub at https://github.com/eserie/wax-ml.