MLops_with_Cirun
awesome-mlops
MLops_with_Cirun | awesome-mlops | |
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1 | 24 | |
0 | 11,818 | |
- | - | |
10.0 | 5.2 | |
over 1 year ago | 5 days ago | |
Python | ||
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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.
MLops_with_Cirun
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MLOps on GitHub Actions with Cirun
This workflow created jobs for two runners, one is for self-hosted using GPU and another one is for GitHub action runner using CPU. To review the latest version of this workflow in this repository.
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.
What are some alternatives?
mlops-github-actions-cirun - MLOps on GitHub Actions using Cirun.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
kserve - Standardized Serverless ML Inference Platform on Kubernetes
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
Awesome-Federated-Learning - FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
applied-ml - 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
awesome-mlops - :sunglasses: A curated list of awesome MLOps tools
bodywork - ML pipeline orchestration and model deployments on Kubernetes.
vertex-ai-samples - Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud
Cookbook - The Data Engineering Cookbook
kubeflow-learn
kind - Kubernetes IN Docker - local clusters for testing Kubernetes