paradigm
metaflow
paradigm | metaflow | |
---|---|---|
9 | 24 | |
36 | 7,607 | |
- | 1.2% | |
7.6 | 9.2 | |
11 months ago | 1 day 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.
paradigm
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Deploying speech recognition models at scale
I built Paradigm from scratch to deploy any model at scale. It deploys the model on Kubernetes with load balancers. If you run into any issues, I'm happy to guide you on how to use it.
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Which is the best MLOps tool for getting started?
I started with paradigm. I got a deeper understanding about argo workflows through it as well. Helps to get a proper grab of industry standards from the beginning.
- What are some open-source ML pipeline managers that are easy to use?
- I use this OS tool to deploy LLMs on Kubernetes.
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Serving Scikit-Learn model on EC2 instance and Scaling
For scalability, it should be on Kubernetes. This is the best solution I have come across. You can deploy the model as a service with a LoadBalancer. You can refer to Kubernetes services or use a tool such as this one that handles building the service for you.
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Who wants to run ML pipelines on Kubernetes? This might be the simplest tool for the job.
I came across this tool today and checked it out, I feel this can get the job done very quickly without so many complex features. It is also very small in size, so does not take up a lot of space in the cluster as well.
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[P] I found the simplest tool to run ML pipelines on Kubernetes. Github link in comments.
Link - https://github.com/ParadigmAI/paradigm It seems to be a pretty new project. But this has high usability.
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Airflow + Slurm for ML Training Pipelines?
Prefect is a good choice, But I wanted a much simpler tool. Hence, I built a barebone workflow controller here.
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?
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
flecs - A fast entity component system (ECS) for C & C++
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
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]
aws-sfn-resume-from-any-state - Resume failed state machines midstream and skip all previously succeded steps.
kedro-great - The easiest way to integrate Kedro and Great Expectations
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
dagster - An orchestration platform for the development, production, and observation of data assets.
dvc - 🦉 ML Experiments and Data Management with Git