machine_learning_examples
polyaxon
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| machine_learning_examples | polyaxon | |
|---|---|---|
| 3 | 9 | |
| 8,072 | 3,479 | |
| - | 0.7% | |
| 5.3 | 8.7 | |
| 27 days ago | 4 days ago | |
| Python | Python | |
| - | Apache License 2.0 |
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machine_learning_examples
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Doubt about numpy's eigen calculation
Does that mean that the example I found on the internet is wrong (I think it comes from a DL Course, so I'd imagine it is not wrong)? or does it mean that I am comparing two different things? I guess this has to deal with right and left eigen vectors as u/JanneJM pointed out in her comment?
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How to save an attention model for deployment/exposing to an API?
I've been following a course teaching how to make an attention model for neural machine translation, This is the file inside the repo. I know that I'll have to use certain functions to make the textual input be processed in encodings and tokens, but those functions use certain instances of the model, which I don't know if I should keep or not. If anyone can please take a look and help me out here, it'd be really really appreciated.
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?
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
MLflow - Open source platform for the machine learning lifecycle
applied-ml - 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
kubeflow - Machine Learning Toolkit for Kubernetes
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
neptune-client - 📘 The MLOps stack component for experiment tracking
dvc - 🦉 ML Experiments and Data Management with Git
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
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.