aiyu
awesome-mlops
| aiyu | awesome-mlops | |
|---|---|---|
| 8 | 24 | |
| 13 | 11,769 | |
| - | - | |
| 7.2 | 5.2 | |
| about 1 year ago | 15 days ago | |
| - | - |
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.
aiyu
- Core shell functions for AI. I wrote a library that allows you to easily use the most powerful AI's project to date.
- Aiyu, a set of shell functions that wraps around all major AI projects and interconnects them. It's a shell function!
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It's getting silly guys
Give this a try then https://github.com/GabrieleRisso/aiyu
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?
awesome-artificial-intelligence - A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
ML-YouTube-Courses - 📺 Discover the latest machine learning / AI courses on YouTube.
kserve - Standardized Serverless ML Inference Platform on Kubernetes
rgpt - An insane cli ChatGpt client written in Rust
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
sandlib
Awesome-Federated-Learning - FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
gold-miner - 🥇掘金翻译计划，可能是世界最大最好的英译中技术社区，最懂读者和译者的翻译平台：
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.