applied-ml
Cookbook
applied-ml | Cookbook | |
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13 | 21 | |
25,984 | 12,945 | |
- | - | |
3.0 | 7.8 | |
4 days ago | about 2 months ago | |
MIT License | Apache License 2.0 |
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applied-ml
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[D] Favorite ML Youtube Channels/Blogs/Newsletters
Also, have any of you stumbled across any cool GitHub repos like this one: https://github.com/eugeneyan/applied-ml ?
- Curated Papers on Machine Learning in Production
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Top Github repo trends in 2021
The second repo I LOVE is Eugene Yan’s Applied ML repository. This is a brilliant idea to create and actually something I was planning on sort of casually doing in my non-existent free time… Anyhow, it is a curated list of technical posts from top engineering teams (Netflix, Amazon, Pinterest, Linkedin, etc.) detailing how they built out different types of AI/ML systems (e.g. forecasting, recommenders, search and ranking, etc.). Ofc, it focuses on AI/ML, but something similar could be made for the traditional or BI-oriented analytics stack, as well as the streaming world, super high value for practitioners! Btw-one of my favorite things at BCG used to be looking at our IT architecture team’s reference architecture diagrams… the best way to understand technologies is to look at how a ton of stuff is architected… and its fun!
- Curated papers, articles, & blogs on data science and ML in production
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Messed up my career by pivoting to DS. Wondering if it's too late to switch to MLE
Applied ML: A collection of papers, articles, and blogs on ML in production by different companies (Netflix, Uber, Facebook, LinkedIn, etc)
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[D] A dilemma of an ML guy in industry
Eugene Yan's applied-ml has tons of case studies.
- Papers & tech blogs by companies sharing their work on data science & machine learning in production.
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My information dump for people trying to break into data science/interview notes
https://github.com/eugeneyan/applied-ml You may find some of his links interesting. I would avoid anything that refers to scaling up a platform as these are more backend engr focus. The more relevant posts to you are probably on the scale of blog posts that are product oriented like the ones I listed in section 4 (e.g. we wanted to solve X for our users and this is how we scoped and defined it). The technical aspects should come backseat to the business aspects. There's def a lot of companies/blog posts that he missed, but the internet is huge.
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[D] Can anyone point me to resources/case studies of companies/business creating infrastructure for their data needs?
Check the resources mentioned in applied-ml. It includes blog posts/papers from many companies describing how they built some ML product X.
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What content would be useful to intermediate Data Scientist
Check out this repo. They collect hundreds of case studies, broken down by dozens of methodologies from large real-world companies such as AirBnB, Nvidia, Uber, Netflix etc.
Cookbook
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Tranzitie catre data engineering
https://github.com/andkret/Cookbook arunca un ochi aici. Omul are si youtube channel https://www.youtube.com/@andreaskayy
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How do i become a data engineer?
I can recommend https://learndataengineering.com by Anreas Krenz. Will guide you via all important topics starting from sql & python to building pipelines using AWS/GCP. I used to participate for 1 year (costs ~ 200 Euro/220$). It's a self-paced. So for ~15h/week you can switch into DE position for appr. 6 months.
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I start my first day as a Data Engineer next Monday, any tips?
I wonder if anyone involved in this post and comments have tried this? https://learndataengineering.com/
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Data engineering certificates
I think it's allowed: https://learndataengineering.com
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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.
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Furthering SQL career
I am doing this currently to fill in the blanks: https://learndataengineering.com. Also, do you know Python? If not take class on Udemy on that. Finally, data engineering is all about tools these days. I saw someone recommended this book here: Data Engineering with Python, I find it super hopeful. You download these tools (Apache Airflow, etc) and get a go with it. I am going to build some data pipelines via this book :)
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Any online bachelor/masters degree to recommend for data engineering?
the best way to be a dev or DE is to build stuff, not learning about algorithms. Just google DE academy, bootcamp or so. The linked one is quite good for a cheap price. A degree prepares you mostly for a PhD, not for a job. So dont look for degrees preparing you for a job in general.
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Beginner DE Courses on Coursera/Udemy?
I usually don't do self promotion, but because you directly asked for a good source. Look at my academy: https://learndataengineering.com
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Women in data engineering
Find something like https://learndataengineering.com/, udemy or any other 'bootcamp/course' that goes on for few months and learn it. It is important that you will have some mentors or study buddies to exchange ideas or so.
- Data Engineering - consigli
What are some alternatives?
awesome-mlops - A curated list of references for MLOps
data-engineering-zoomcamp - Free Data Engineering course!
awesome-ml-blogs - Curated list of technical blogs on machine learning · AI/ML/DL/CV/NLP/MLOps
Shuffle - Shuffle: A general purpose security automation platform. Our focus is on collaboration and resource sharing.
machine-learning-roadmap - A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
data-engineering-book - Accumulated knowledge and experience in the field of Data Engineering
ml-surveys - đź“‹ Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.
Coursera-Clone - Coursera clone
pipebase - data integration framework
data-engineer-roadmap - Roadmap to becoming a data engineer in 2021
self-hosted-cookbook - A cookbook, for docker-compose based recipes, for self-hosted applications and services.