Name
Abstract | ||
|---|---|---|
This paper outlines our Deep Learning Lifecycle Data Management system. It consists of two major parts: the LDM Core Tool - a simple data logging tool; and an Extension Mechanism - this mechanism allows the user to extend the simple LDM Core Tool to match their specific requirements. Current extensions support adding new visualisations for data stored on the server. Our approach allows the Core Tool to be a complete black box; we need only a metamodel denoting the logical structure of the stored data. By then specialising this metamodel we can define an Extension Metamodel which, when communicated to the tool through configuration, allows us to define and thus add the extensions. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.22364/bjmc.2022.10.1.02 | BALTIC JOURNAL OF MODERN COMPUTING |
Keywords | DocType | Volume |
model driven architecture, metamodel specialisation, MLOps, software extension, deep learning | Journal | 10 |
Issue | ISSN | Citations |
1 | 2255-8942 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paulis Barzdins | 1 | 0 | 0.68 |
| Audris Kalnins | 2 | 0 | 1.01 |
| Edgars Celms | 3 | 0 | 0.68 |
| Janis Barzdins | 4 | 199 | 35.69 |
| Arturs Sprogis | 5 | 0 | 0.68 |
| Mikus Grasmanis | 6 | 0 | 0.34 |
| Sergejs Rikacovs | 7 | 0 | 0.34 |
| Guntis Barzdins | 8 | 121 | 18.62 |