mllint
awesome-seml
| mllint | awesome-seml | |
|---|---|---|
| 3 | 1 | |
| 72 | 1,195 | |
| - | 0.9% | |
| 3.8 | 0.0 | |
| almost 2 years ago | about 1 month ago | |
| Go | ||
| GNU General Public License v3.0 only | Creative Commons Zero v1.0 Universal |
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.
mllint
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[D] How to maintain ML models?
Finally, there is the mllint tool that I have been developing during my MSc thesis on Software Quality in ML projects. While still a research prototype, it can already analyse your project and may be able to provide you with practical recommendations on what tools & techniques to employ for several aspects of your ML project's development. Feel free to try it out on your project and let me know what you think of it!
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Last week fluff-free AI, ML, and data-related original articles summary
- OpenAI released an improved version of Codex - Command-line utility to evaluate the technical quality of ML projects written in Python - It takes a whole convolutional neural network with five to eight layers to approximate a single cortical neuron - MLOps Monitoring Market Review
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[P][R] Announcing `mllint` — a linter for ML project software quality.
Source: https://github.com/bvobart/mllint
awesome-seml
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[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.
What are some alternatives?
mlnotify - 🔔 No need to keep checking your training - just one import line and you'll know the second it's done.
MLOps - MLOps examples
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.
CortexTheseus - Cortex - AI on Blockchain, Official Golang implementation
MLflow - Open source platform for the machine learning lifecycle
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
dvc - 🦉 ML Experiments and Data Management with Git
gorse - Gorse open source recommender system engine
awesome-vulnerability-assessment - An ever-growing list of resources for data-driven vulnerability assessment and prioritization
dslinter - `dslinter` is a pylint plugin for linting data science and machine learning code. We plan to support the following Python libraries: TensorFlow, PyTorch, Scikit-Learn, Pandas and NumPy.
Lossless - Lossless is a Machine Learning library built for Golang, capable of handling MLPs, CNNs, and more soon.