clearml
kedro-great
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| clearml | kedro-great | |
|---|---|---|
| 20 | 1 | |
| 5,202 | 52 | |
| 2.2% | - | |
| 8.1 | 0.0 | |
| 8 days ago | over 1 year ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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.
clearml
- FLaNK Stack Weekly 12 February 2024
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clearml VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
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[D] Drop your best open source Deep learning related Project
Hi there. ClearML is our open-source solution which is part of the PyTorch ecosystem. We would really appreciate it if you read our README and starred us if you like what you see!
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[D] Facebook Visdom vs Google Tensorboard for Pytorch
I'm talking about ClearMLđ trying not to shill for open-source but ~5000 teams have already chosen đŞ https://github.com/allegroai/clearml
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[D] Whatâs the simplest, most lightweight but complete and 100% open source MLOps toolkit? -> MY OWN CONCLUSIONS
There are mainly two solutions that are 100% open source and free to install and use, and that may solve most of the requirements of ML practitioners: Hopsworks and ClearML. Among this two, if I had to chose one right now, it will be ClearML. Hopsworks might be much more complete, but ClearML seems to have a bigger community behind it and to be easier to install and use. So ClearML will be something to take a look at in case we go for an all-in-one package. I also like the idea of having a platform with an UI with all our projects.
- [D] Whatâs the simplest, most lightweight but complete and 100% open source MLOps toolkit?
kedro-great
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[D] Whatâs the simplest, most lightweight but complete and 100% open source MLOps toolkit? -> MY OWN CONCLUSIONS
I expected Great Expectations library to be recommended, but nobody told anything. Instead, unit testing and/or smoke tests using pytest. And checking them with Jenkins. Anyway, if Kedro ends up being our project template, I'll keep an eye on the plugin with Great Expectations.
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
great_expectations - Always know what to expect from your data.
Poetry - Python packaging and dependency management made easy
streamlit - Streamlit â A faster way to build and share data apps.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
feast - Feature Store for Machine Learning
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
ploomber - The fastest âĄď¸ way to build data pipelines. Develop iteratively, deploy anywhere. âď¸