Gems-status (http://github.com/jordimassaguerpla/gems-status) is a command line tool that creates a report about the gems used in an appliacation. The most import result is the security alerts.
However, this tool is being configured using a yaml file, which has to be updated with new information every time there is a security alert.
The docker way of updating containers is to build a new image with the updated binaries and files, which creates a security concern.
The docker way is not anymore running "zypper update" in the containment but to update the whole image in the image registry (hub docker if we are talking about public registry) and then pull the image update from there, stop the outdated containments and replace them by starting new containments based on the new image.
The goal of this project is to get an overview of the state-of-the-art technology on training and deploying machine learning projects with kubernetes and apply that to a SUSE CaaSP cluster.
With that in mind, we will train and deploy a model for summarizing github issues:
I once had a bad dream.
I started good, a sunny day. I had just fixed an issue and push it to my fork, in order to create a Pull Request. I was happy. It felt awesome to have found a fix so elegant. Two lines of code.
MONAI is a set of open-source, freely available collaborative frameworks built for accelerating research and clinical collaboration in Medical Imaging. The goal is to accelerate the pace of innovation and clinical translation by building a robust software framework that benefits nearly every level of medical imaging, deep learning research, and deployment.
MONAI Deploy aims to become the de-facto standard for developing packaging, testing, deploying, and running medical AI applications in clinical production. MONAI Deploy creates a set of intermediate steps where researchers and physicians can build confidence in the techniques and approaches used with AI — allowing for an iterative workflow.