Awesome-Federated-Learning
FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai (by chaoyanghe)
awesome-mlops
A curated list of references for MLOps (by visenger)
| Awesome-Federated-Learning | awesome-mlops | |
|---|---|---|
| - | 24 | |
| 1,848 | 11,818 | |
| - | - | |
| 0.0 | 5.2 | |
| over 1 year ago | 6 days ago | |
| - | - |
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-Federated-Learning
Posts with mentions or reviews of Awesome-Federated-Learning.
We have used some of these posts to build our list of alternatives
and similar projects.
We haven't tracked posts mentioning Awesome-Federated-Learning yet.
Tracking mentions began in Dec 2020.
awesome-mlops
Posts with mentions or reviews of awesome-mlops.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-07.
- MLOps
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ML Engineer Roadmap
I'm in the same boat. Data scientist shifting towards ML engineering-MLOps. The guide seems quite complete. I am also doing the ML DevOps engineer, which has end-to-end projects and has been helpful so far. I also feel that most ML engineers will be Mlops too, as most companies will not distinguish between the two, so I try to focus on this part. Here is a quite comprehensive list of resources: https://github.com/visenger/awesome-mlops
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Mlops roadmap
Good Reference: https://github.com/visenger/awesome-mlops (The Link above has so many Guides, It's insane) https://madewithml.com/
- What do data scientists use Docker for?
-
Do you wonder why MLOps is not at the same level as DevOps?
I recently did a deep-dive into MLOps for a client, and I've found that https://ml-ops.org/ offers a great overview. Some topics are a bit too "zoomed out", but they still touch on most important aspects of building an end-to-end product. I found it a great starting point when doing research, and picking and choosing some key points from each section + some google helped a lot. Give it a look, you'll probably find some useful things in there.
- Can you guys explain to me what MLOps is?
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MLOps on GitHub Actions with Cirun
MLOps
- DevOps - where to begin?
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JBCNConf 2022: A great farewell
She made mentions to ML-Ops and MLFlow including Vertex AI the GCP implementation. I will post the video as soon as it is available. In the meantime, you can enjoy any other talk from Nerea Luis
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Can Mechanical Engineers become MLOps?
From your post, you seem to be trained for data science for physics modeling, so I'd recommend to get started with https://ml-ops.org/ and for the data engineering part, I found this https://github.com/andkret/Cookbook open source cookbook to be invaluable.
What are some alternatives?
When comparing Awesome-Federated-Learning and awesome-mlops you can also consider the following projects:
autogluon - Fast and Accurate ML in 3 Lines of Code
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!