- This is developed for wildfire analysis to begin with
- Architected in a way that it can be adapted to other ML problems.
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was supported by the LLNL-LDRD Program under Project No. 22-SI-008.
This work was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or Lawrence Livermore National Security, LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or Lawrence Livermore National Security, LLC, and shall not be used for advertising or product endorsement purposes.
This software package is an Unclassified/Open-Source Distribution under the terms of the MIT license and has been approved by Lawrence Livermore National Laboratory for unrestricted release.
- Release ID: LLNL-CODE-2001016
- Title: Machine Learning Automation Pipeline (MLAP), v 1.0
- Author(s): Pankaj Jha