modelstore
polyaxon
| modelstore | polyaxon | |
|---|---|---|
| 4 | 9 | |
| 353 | 3,483 | |
| 1.1% | 0.4% | |
| 6.9 | 8.7 | |
| 3 months ago | 9 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.
modelstore
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An open source ML model registry called modelstore
A couple of years ago (during a lockdown!), I started working on modelstore - a Python library for managing trained machine learning models.
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lightweight model performance tracking?
The lightest open source option I'm aware of is https://github.com/operatorai/modelstore
- What us the best MLOps platform for 2022
- [D] using git or other tools to manage models
polyaxon
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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.
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[D] Kubernetes for ML - how are y'all doing it?
We use Polyaxon and it’s pretty good
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[D] What MLOps platform do you use, and how helpful are they?
Disclosure - I'm the author of Polyaxon.
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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.
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[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.
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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?
onnxmltools - ONNXMLTools enables conversion of models to ONNX
MLflow - Open source platform for the machine learning lifecycle
gato - Unofficial Gato: A Generalist Agent
kubeflow - Machine Learning Toolkit for Kubernetes
wandb - 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
dvc - 🦉 ML Experiments and Data Management with Git
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
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