mlops-course
AntiPython-AI-Club
| mlops-course | AntiPython-AI-Club | |
|---|---|---|
| 20 | 2 | |
| 2,741 | 28 | |
| - | - | |
| 2.1 | 10.0 | |
| 9 months ago | 3 months ago | |
| Jupyter Notebook | C | |
| MIT License | MIT License |
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mlops-course
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Ask HN: Daily practices for building AI/ML skills?
coming from a similar context, i believe going top down might be the way to go.
up to your motivation, doing basic level courses first (as shared by others) and then tackling your own application of the concepts might be the way to go.
i also observe the need for strong IT skills for implementing end-to-end ml systems. so, you can play to your strenghts and also consider working on MLOps. (online self-paced course - https://github.com/GokuMohandas/mlops-course)
i went back to school to get structured learning. whether you find it directly useful or not, i found it more effective than just motivating myself to self-learn dry theory. down the line, if you want to go all-in, this might be a good option for you too.
- [Q] Any good resources for MLOps?
- Open-Source Machine Learning for Software Engineers Course
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Open-source MLOps Fundamentals Course 🚀
Find all the lessons here → https://madewithml.com/MLOps course repo → https://github.com/GokuMohandas/mlops-courseMade With ML repo → https://github.com/GokuMohandas/Made-With-ML
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What are examples of well-organized data science project that I can see on Github?
- https://github.com/GokuMohandas/mlops-course (code for MLOps course)
- Made With ML – develop, deploy and maintain production machine learning
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Where can I learn more about the engineering part of the role?
Haven’t done it but have heard good reviews - https://github.com/GokuMohandas/mlops-course
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Path to ML from a backend engineering role
If MLOps, read https://github.com/GokuMohandas/mlops-course 😎
- What skills should I focus on to improve as a MLE?
- MadeWithML – A practical approach to learning machine learning
AntiPython-AI-Club
- Anti Python AI Club: AI for Python H8ers
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Ask HN: Daily practices for building AI/ML skills?
If you're interested in AI but dislike Python you can join the Anti Python AI club here: https://github.com/Fileforma/AntiPython-AI-Club
We work together to build AI models in our favorite programming languages.
What are some alternatives?
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
mlops-with-vertex-ai - An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
machine-learning-interview - Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
fastai - The fastai deep learning library
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
cookiecutter-data-science - A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
ml-mipt - Former repository of ML course. Redirect link included
eemeter - An open source python package for implementing and developing standard methods for calculating normalized metered energy consumption and avoided energy use.
eo-learn - Earth observation processing framework for machine learning in Python
ml-pipeline-engineering - Best practices for engineering ML pipelines.