glossary
Data Glossary 🧠: An interactive digital garden for deeper data exploration. Learn through a graph and backlinks, enabling layered knowledge discovery. (by airbytehq)
applied-ml
đź“š Papers & tech blogs by companies sharing their work on data science & machine learning in production. (by eugeneyan)
glossary | applied-ml | |
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5 | 13 | |
90 | 26,050 | |
- | - | |
4.3 | 3.0 | |
10 months ago | 19 days ago | |
SCSS | ||
MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
glossary
Posts with mentions or reviews of glossary.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-07-17.
- Data Engineering Glossary
- A Single Place for All Data Knowledge
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Data Engineering Concepts: Definitions, Backlinks, and Graph View
The difference is, the data glossary does not need a paid and closed-source Obsidian Publish for publishing. The data glossary is fully open and uses GoHugo and Quartz.
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Want to transition to data engineering but are overwhelmed with all the terms?
All of it is open on GitHub. Feels free to add missing terms or ask questions.
applied-ml
Posts with mentions or reviews of applied-ml.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-12.
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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.