awesome-seml
A curated list of articles that cover the software engineering best practices for building machine learning applications. (by SE-ML)
awesome-vulnerability-assessment
An ever-growing list of resources for data-driven vulnerability assessment and prioritization (by lhmtriet)
| awesome-seml | awesome-vulnerability-assessment | |
|---|---|---|
| 1 | 1 | |
| 1,195 | 78 | |
| 0.9% | - | |
| 0.0 | 2.3 | |
| about 1 month ago | about 1 year ago | |
| Creative Commons Zero v1.0 Universal | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-seml
Posts with mentions or reviews of awesome-seml.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-16.
-
[D] How to maintain ML models?
They also have an awesome-seml repo on GitHub outlining many (scientific) articles as well as tools and frameworks that may help you out in implementing these best practices.
awesome-vulnerability-assessment
Posts with mentions or reviews of awesome-vulnerability-assessment.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Seeking Advice on Developing a Vulnerability Management Program
At first glance the tool selection looks a bit counterintuitive - will your focus be EASM, vulnerability assessment (you are not managing anything unless you include risk acceptance/mitigation and remediation) or automated (atomic) red teaming? For easy exploitability checks have a look at Prelude Operator; Nuclei as a modern scanner, OpenVAS to represent the traditional approach. For theory backing check here: https://github.com/lhmtriet/awesome-vulnerability-assessment
What are some alternatives?
When comparing awesome-seml and awesome-vulnerability-assessment you can also consider the following projects:
MLOps - MLOps examples
subaru-starlink-research - Subaru StarLink persistent root code execution.
yt-channels-DS-AI-ML-CS - A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.
MLflow - Open source platform for the machine learning lifecycle
dvc - 🦉 ML Experiments and Data Management with Git
mllint - `mllint` is a command-line utility to evaluate the technical quality of Python Machine Learning (ML) projects by means of static analysis of the project's repository.