Realizing End to End Reproducible Machine Learning on Kubernetes
Industry adaptation of data-science has grown rapidly in the last few years. The probabilistic nature of this space requires the right tools and techniques to ensure that the answers produced are reli …
| Talk Title | Realizing End to End Reproducible Machine Learning on Kubernetes |
| Speakers | Suneeta Mall (Senior Data Scientist, Nearmap) |
| Conference | KubeCon + CloudNativeCon North America |
| Conf Tag | |
| Location | San Diego, CA, USA |
| Date | Nov 15-21, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Industry adaptation of data-science has grown rapidly in the last few years. The probabilistic nature of this space requires the right tools and techniques to ensure that the answers produced are reliable. Models are derived from data, which is almost always evolving, massive (as in deep-learning), and requiring clean-up and pre-processing before use. Reproducibility, reporting, tracking and management around the tasks of 1) data - collection, pre-processing, often feature engineering and 2) model – training, tuning, evaluation and serving are essential.With tools such as Pachyderm, Kubeflow, Katib, ModelDB, Seldon and Argo, an automated end-to-end reproducible machine learning framework can be built on Kubernetes. This talk will detail how the aforementioned tools can be used to build an automated, reproducible machine learning framework.
