soorgeon
polyaxon
| soorgeon | polyaxon | |
|---|---|---|
| 3 | 9 | |
| 75 | 3,491 | |
| - | 0.7% | |
| 4.0 | 8.7 | |
| 4 months ago | 20 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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.
soorgeon
-
Tips and Tricks to Use Jupyter Notebooks Effectively
If you're looking to improve your Jupyter workflow, check out Ploomber's open-source projects: Ploomber for developing modular data pipelines, Soorgeon for refactoring and cleaning), or nbsnapshot for notebook testing.
- Soorgeon - automated Jupyter notebook refactoring
-
How to turn my data processing code into a pipeline?
If you're working with .py or .ipynb I recommend ploomber, there's lots of flexibility on the input/output formats and you can interactively check out your data and work with Git. There's also an automatic tool that converts it to a pipeline for you via the H2 headings.
polyaxon
-
Any MLOps platform you use?
If you're not concerned about self-hosting, WandB is one of the more fully featured training monitoring tools (I've used it in the past without any issues but the lack of data and training privacy and lack of self-hosting possibilities makes it a hard no for anything that isn't scholastic). Polyaxon is an alternative but rewriting all your variable logging to conform to their requirements makes it very difficult to switch to it in the middle of a project so you have to commit to it from the get-go.
-
[D] Kubernetes for ML - how are y'all doing it?
We use Polyaxon and it’s pretty good
-
[D] What MLOps platform do you use, and how helpful are they?
Disclosure - I'm the author of Polyaxon.
-
Does anyone have experience with polyaxon?
I just came across https://github.com/polyaxon/polyaxon because mlflow gives me a hard time and costs my company money by the day because it is not working as expected.
-
[D] Productionalizing machine learning pipelines for small teams
For running experiments, http://polyaxon.com/ is a really good free open-source package that has lots of nice integrations so you can quickly run experiments in k8s but it might be overkill in some cases.
-
Top 5 tools to get started with MLOps !
Polyaxon : https://polyaxon.com
- Open source alternative to AWS Sagemaker, Google AI Platform, and Azure ML
What are some alternatives?
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
MLflow - Open source platform for the machine learning lifecycle
mlrun - MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
kubeflow - Machine Learning Toolkit for Kubernetes
feast - The Open Source Feature Store for Machine Learning
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
nbsnapshot - Automated Jupyter notebook testing. 📙
dvc - 🦉 ML Experiments and Data Management with Git
dagster - An orchestration platform for the development, production, and observation of data assets.
onepanel - The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.
mmlspark - Simple and Distributed Machine Learning [Moved to: https://github.com/microsoft/SynapseML]
neptune-client - 📘 The MLOps stack component for experiment tracking