awesome-mlops
Awesome-Federated-Learning
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awesome-mlops | Awesome-Federated-Learning | |
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24 | - | |
11,719 | 1,848 | |
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4.9 | 0.0 | |
about 2 months ago | over 1 year ago | |
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awesome-mlops
- 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?
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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.
Awesome-Federated-Learning
We haven't tracked posts mentioning Awesome-Federated-Learning yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
autogluon - Fast and Accurate ML in 3 Lines of Code
kserve - Standardized Serverless ML Inference Platform on Kubernetes
awesome-federated-learning - resources about federated learning and privacy in machine learning
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
applied-ml - 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
ben-decentralized-chatbot - YC Hackathon 2018 Winner Project. BEN: A decentralized chatbot that uses federated learning.
awesome-mlops - :sunglasses: A curated list of awesome MLOps tools
tinyml-papers-and-projects - This is a list of interesting papers and projects about TinyML.
bodywork - ML pipeline orchestration and model deployments on Kubernetes.
Aweome-Heathcare-Federated-Learning - A curated list of Federated Learning papers/articles and recent advancements.