metaflow-on-kubernetes-docs
metaflow
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| metaflow-on-kubernetes-docs | metaflow | |
|---|---|---|
| 1 | 24 | |
| 7 | 7,586 | |
| - | 2.5% | |
| 1.8 | 9.2 | |
| about 2 years ago | 5 days ago | |
| Shell | Python | |
| - | Apache License 2.0 |
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metaflow-on-kubernetes-docs
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[D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit? -> MY OWN CONCLUSIONS
There are community Forks supporting Kubernetes and KFP. But they are not yet a part of the main framework and support is fluctuating. I think support should be available in the future.
metaflow
- FLaNK Stack 05 Feb 2024
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metaflow VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
- In Need of Guidance: Implementing MLOps in a Complex Organization as a Junior Data Engineer
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What are some open-source ML pipeline managers that are easy to use?
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home
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Needs advice for choosing tools for my team. We use AWS.
1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling.
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Selfhosted chatGPT with local contente
even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf.
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[OC] Gender diversity in Tech companies
They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural networks to downscale video.. Sound familiar? That’s cause that’s practically the same thing as Nvidia’s DLSS (which upscales instead of downscales).
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Model artifacts mess and how to deal with it?
Check out Metaflow by Netflix
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Going to Production with Github Actions, Metaflow and AWS SageMaker
Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project.
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Small to Reasonable Scale MLOps - An Approach to Effective and Scalable MLOps when you're not a Giant like Google
It's undeniable that leadership is instrumental in any company and project success, however I was intrigued with one of their ML tool choices that helped them reach their goal. I was so curious about this choice that I just had to learn more about it, so in this article will be talking about a sound strategy of effectively scaling your AI/ML undertaking and a tool that makes this possible - Metaflow.
What are some alternatives?
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
metaflow - Build and manage real-life data science projects with ease.
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
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!
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
streamlit - Streamlit — A faster way to build and share data apps.
kedro-great - The easiest way to integrate Kedro and Great Expectations
great_expectations - Always know what to expect from your data.
dvc - 🦉 ML Experiments and Data Management with Git