Pushing back on the cult of complexity.
Some off-the-beaten uses of Python learned from reading libraries.
Understanding and spotting patterns to use code and components as intended.
What to consider for in terms of data, roadmap, role, manager, tooling, etc.
Why this is the first rule, some baseline heuristics, and when to move on to machine learning.
Breaking it into offline vs. online environments, and candidate retrieval vs. ranking steps.
An overview and comparison of the various approaches, with examples from industry search systems.
Access, serving, integrity, convenience, autopilot; use what you need.
Stop procrastinating, go off the happy path, learn just-in-time, and get your hands dirty.
Why real-time? How have China & US companies built them? How to design & build an MVP?
What questions do they answer? How do they compare? What open-source solutions are available?
Why (and why not) be more end-to-end, how to, and Stitch Fix and Netflix's experience
Why OMSCS? How can I get accepted? How much time needed? Did it help your career? And more...
I couldn't find any guides on serving HTML with FastAPI, thus I wrote this to plug the hole on the internet.
After this article, we'll have a workflow of tests and checks that run automatically with each git push.
Using a Zettelkasten helps you make connections between notes, improving learning and memory.
How hard work, many failures, and a bit of luck got me into the field and up the ladder.
A deeper look into the strengths and weaknesses of Agile in Data Science projects (Part 1 of 2).