Ml-Engineering on The Edge Cases

Machine Learning System Design Interview

Sat, 15 Apr 2023 02:43:54 +0000

Note: See here for valid statuses

[author: Ali Aminian Alex Xu]

Summary

What are the key ideas? How can I apply this knowledge that I learned? How do these ideas relate to what I already know?

💡Ideas

Note: These should become Project Idea fleeting notes with a link back to this source.

Fleeting Notes

Note: Conceptual notes that aren’t as tied to the source material. These are fodder for processing into evergreen notes.

Task Machine Learning Engineers and Platform MLEs

Sun, 18 Dec 2022 03:31:19 +0000

Note

Task MLEs work with data scientists to “productionize” models, sustaining a specific pipeline or pipelines in production. They need to write, maintain, and monitor pipelines that are responsible for the entire lifecycle of a ML model.

Platform MLEs help task MLEs automate their job. They build pipelines that support multiple tasks (task MLEs solf specific tasks).

Platform MLEs build pipelines to create features, task MLEs create pipelines to use features. PMLEs trigger ML performance drop alerts, TMLEs act on alerts.

Database Engineering and Machine Learning Systems for Data Science Curriculum

Fri, 18 Feb 2022 00:47:25 +0000

This is a list of resources to gain an advanced understanding of databases and machine learning systems that data engineers, MLOps professionals, ML engineers, and other titles build for R&D groups. Because this is a major part of my own job description, I wanted to compile this list of courses to be consumed as projects to improve what I build for my own teams.

The first iteration of this comes completely from Arun Kumar’s response thread to me asking about how to learn these things without shelling out for an MS in CS (still a goal of mine, though!).