climate-change-data
metaflow
climate-change-data | metaflow | |
---|---|---|
2 | 24 | |
537 | 7,630 | |
- | 1.8% | |
0.6 | 9.2 | |
5 months ago | 6 days ago | |
Python | ||
- | 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.
climate-change-data
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5 Ways to Celebrate Earth Day as a Developer 🌎🌏🌍
A couple of sources for climate-related open data is OpenClimateData.net which is a curated list of sources for open emissions and climate agreements data that are made public by various NGOs and the UN. Another great source for open data is this GitHub repo by Dr. Kasia Kulma, as per her bio a data scientist focused on using data for good. The repo is extensive and not only does it offer open data, but many APIs and open source projects as well. Check out the repo’s README to see the long list of links.
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Honey bees at risk for colony collapse from longer, warmer fall seasons
Are you denying human caused climate change because you don't think there's enough data to support it? You believe the data supporting climate change theories only dates from 1979? Well, great news, I have lots of reading that will quickly get you up to date and help you understand that climate change isn't demonstrated only through satellite models!
In the IPCC Sixth Assessment Report introduction, you can see the various methodologies used to demonstrate the effects of human-caused climate change: https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-1/#1.3
For example, we've been taking thermometer and barometer observations at Earth's surface since the 1600s. By the 1800s this was widely distributed through naval weather logs. For atmospheric readings, we've been getting those since the 40's, not 70s, because that's when we invented weather balloons :) but we have even further back information, through the field of Paleoclimate, where we do things like measuring C02 concentration of bubbles in polar ice sheets, dating back as much as 800,000 years ago. We can also use tree rings (hundreds of years, or thousands if fossilized), "corals, stalactites and stalagmites, dust sediments, fossil pollen, peat, lake sediment, and marine sediment" to measure climate change over millenia, no need for satellites!
Want to play with all the data on your own? https://github.com/KKulma/climate-change-data Her's a curated list of all sorts of data you can play with. https://openclimatedata.net/ Here's some jupyter notebooks!
What data is missing that you think it's "not enough?"
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?
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
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]
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
dvc - 🦉 ML Experiments and Data Management with Git
great_expectations - Always know what to expect from your data.
feast - The Open Source Feature Store for Machine Learning
Poetry - Python packaging and dependency management made easy
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
awesome-mlops - A curated list of references for MLOps
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.