ml-mipt
mlops-course
| ml-mipt | mlops-course | |
|---|---|---|
| 18 | 20 | |
| 8 | 2,741 | |
| - | - | |
| 0.0 | 2.1 | |
| over 1 year ago | 9 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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ml-mipt
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
What are some alternatives?
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
MachineLearningWithPython - Get started with Machine Learning with Python - An introduction with Python programming examples
mlops-with-vertex-ai - An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
pytorch-implementations - A collection of paper implementations using the PyTorch framework
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
Deep-Learning-Computer-Vision - My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.
machine-learning-interview - Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
ML-Workspace - 🛠All-in-one web-based IDE specialized for machine learning and data science.
fastai - The fastai deep learning library