Hi all,
We are using different descriptions at various places. It would be better
to exemplify each term more clearly. Sorry, If I am asking something
obvious.
1. Which one should we use as the project description?
note: Although, description given in the SystemDS research paper can
be considered - the paper was published before the Merge into SystemML.
2. Also, what is the full form of DML?
a. Declarative machine Learning Language
b. Descriptive Machine Learning Language
c. ..
Research paper [1]:
SystemDS: A Declarative Machine Learning System for the End-to-End Data
Science Lifecycle
GitHub
Apache SystemDS - A versatile system for the end-to-end data science
lifecycle
PyPI
SystemDS is a distributed and declarative machine learning platform.
systemds.apache.org
A machine learning platform optimal for big data
Jira
Apache SystemDS - An open source ML system for the end-to-end data science
lifecycle
---
SystemDS game plan [1] major parts:
1. DSL-based, High-level Abstractions: We aim to provide a hierarchy of
abstractions for the different lifecycle tasks as well as users with
different expertise
2. Hybrid Runtime Plans and Optimizing Compiler: To support the wide
variety of algorithm classes, we will continue to provide different
parallelization strategies, enriched by a new backend for federated ML
and privacy enhancing technologies.
3. Data Model - Heterogeneous Tensors: To support data integration and
cleaning primitives in linear algebra programs requires a more generic
data model for handling heterogeneous and structured data. In contrast to
existing ML systems, our central data models are heterogeneous tensors.
[1] https://arxiv.org/abs/1909.02976
[2] Roadmap discussion - https://s.apache.org/systemds-roadmap
Thank you,
Janardhan
