machine-learning-interview
mlops-course
| machine-learning-interview | mlops-course | |
|---|---|---|
| 6 | 20 | |
| 8,065 | 2,741 | |
| - | - | |
| 2.7 | 2.1 | |
| 8 months ago | 9 months ago | |
| Jupyter Notebook | ||
| - | MIT License |
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machine-learning-interview
- [D] Machine Learning Interview Prep
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What skills should I focus on to improve as a MLE?
The coding rounds are usually typical Leetcode fare, but the domain round could be anything. It's usually "how would you design a ML system that would do X" where X is something that the company would care about. I would browse this resource to help you: https://github.com/khangich/machine-learning-interview
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Weekly Entering & Transitioning Thread | 26 Sep 2021 - 03 Oct 2021
Minimum Viable Study Plan for ML interviews
- Best Github Repos you'll ever need to crack any coding interview
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Please help me prepare for the system design portion of an upcoming ML engineer interview
I'm doing MLE interviews right now. I was looking at this https://github.com/khangich/machine-learning-interview.
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[D] How would you prep for ML interview at FAANG?
And https://github.com/khangich/machine-learning-interview
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?
system-design-primer - Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
awesome-interview-questions - :octocat: A curated awesome list of lists of interview questions. Feel free to contribute! :mortar_board:
mlops-with-vertex-ai - An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
system-design-interview - System design interview for IT companies
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
data-science-interviews - Data science interview questions and answers
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
LeetCode - This repository contains the solutions and explanations to the algorithm problems on LeetCode. Only medium or above are included. All are written in C++/Python and implemented by myself. The problems attempted multiple times are labelled with hyperlinks.
fastai - The fastai deep learning library
interview - Everything you need to prepare for your technical interview
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱