Abstract
Machine Learning Automation Pipeline (MLAP) is a package to perform machine learning (ML) analysis in
a step by step manner, starting with data extraction until analysis and prediction. The scripts provide the
users option to chose an action such as "Extract", "Prep", and "Train" and numerous cases can be
launched with just a single command. The inputs for each case are provided using a JSON file. The
simulation results of several cases can be assessed using an automated process and analyzed for
various metrics pertinent to ML analysis.
- Developers:
-
Jha, Pankaj [1]
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Release Date:
- 2024-09-18
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Version:
- 1.0
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 157408
- Site Accession Number:
- LLNL-CODE-2001016
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Jha, Pankaj.
Machine Learning Automation Pipeline.
Computer Software.
https://github.com/LLNL/MLAP.
USDOE National Nuclear Security Administration (NNSA).
18 Sep. 2024.
Web.
doi:10.11578/dc.20250630.1.
Jha, Pankaj.
(2024, September 18).
Machine Learning Automation Pipeline.
[Computer software].
https://github.com/LLNL/MLAP.
https://doi.org/10.11578/dc.20250630.1.
Jha, Pankaj.
"Machine Learning Automation Pipeline." Computer software.
September 18, 2024.
https://github.com/LLNL/MLAP.
https://doi.org/10.11578/dc.20250630.1.
@misc{
doecode_157408,
title = {Machine Learning Automation Pipeline},
author = {Jha, Pankaj},
abstractNote = {Machine Learning Automation Pipeline (MLAP) is a package to perform machine learning (ML) analysis in
a step by step manner, starting with data extraction until analysis and prediction. The scripts provide the
users option to chose an action such as "Extract", "Prep", and "Train" and numerous cases can be
launched with just a single command. The inputs for each case are provided using a JSON file. The
simulation results of several cases can be assessed using an automated process and analyzed for
various metrics pertinent to ML analysis.},
doi = {10.11578/dc.20250630.1},
url = {https://doi.org/10.11578/dc.20250630.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20250630.1}},
year = {2024},
month = {sep}
}