awesome-conformal-prediction
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries. (by valeman)
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awesome-conformal-prediction | metaflow | |
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6 | 24 | |
3,381 | 7,586 | |
- | 2.5% | |
9.5 | 9.2 | |
6 days ago | 6 days ago | |
Python | ||
Creative Commons Zero v1.0 Universal | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
awesome-conformal-prediction
Posts with mentions or reviews of awesome-conformal-prediction.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-08.
- Dive Deep into Conformal Prediction with This Ultimate Resource Compilation
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Forecasts need to have error bars
Let me suggest a solution https://github.com/valeman/awesome-conformal-prediction
- A professionally curated list of Conformal Prediction videos, tutorials, & books
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[P] Slice Finder: A framework for discovering explainable, anomalous data subsets
Nice, thanks for sharing! The hole Problem with pointpredictions ist how to measure uncertainty. Check out conformal predictions e.g. awesome conformal predictions https://github.com/valeman/awesome-conformal-prediction and https://mapie.readthedocs.io/en/latest/index.html
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[D]What are some "important" problems in machine learning/AI?
Conformal prediction is the best framework that can deal with uncertainty quantification for most of ML problems. https://github.com/valeman/awesome-conformal-prediction
- awesome-conformal-prediction: A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD theses, articles and open-source libraries.
metaflow
Posts with mentions or reviews of metaflow.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-02-05.
- 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.