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(>50k reads; 21)Improving Recommendation Systems & Search in the Age of LLMs
Model architectures, data generation, training paradigms, and unified frameworks inspired by LLMs.
Evaluating the Effectiveness of LLM-Evaluators (aka LLM-as-Judge)
Use cases, techniques, alignment, finetuning, and critiques against LLM-evaluators.
18 Aug 2024 · 49 min · llm eval production survey 🔥
How to Interview and Hire ML/AI Engineers
What to interview for, how to structure the phone screen, interview loop, and debrief, and a few tips.
07 Jul 2024 · 21 min · machinelearning career leadership 🔥
Prompting Fundamentals and How to Apply them Effectively
Structured input/output, prefilling, n-shots prompting, chain-of-thought, reducing hallucinations, etc.
26 May 2024 · 17 min · llm production 🔥
What We've Learned From A Year of Building with LLMs
From the tactical nuts & bolts to the operational day-to-day to the long-term business strategy.
12 May 2024 · 1 min · llm engineering production leadership 🔥
Patterns for Building LLM-based Systems & Products
Evals, RAG, fine-tuning, caching, guardrails, defensive UX, and collecting user feedback.
30 Jul 2023 · 66 min · llm engineering production 🔥
Experimenting with LLMs to Research, Reflect, and Plan
Also, shortcomings in document retrieval and how to overcome them with search & recsys techniques.
09 Apr 2023 · 14 min · llm deeplearning learning 🛠🔥
Simplicity is An Advantage but Sadly Complexity Sells Better
Pushing back on the cult of complexity.
14 Aug 2022 · 10 min · machinelearning engineering production 🔥
Uncommon Uses of Python in Commonly Used Libraries
Some off-the-beaten uses of Python learned from reading libraries.
31 Jul 2022 · 10 min · python engineering 🔥
Design Patterns in Machine Learning Code and Systems
Understanding and spotting patterns to use code and components as intended.
12 Jun 2022 · 13 min · machinelearning engineering python 🔥
The First Rule of Machine Learning: Start without Machine Learning
Why this is the first rule, some baseline heuristics, and when to move on to machine learning.
19 Sep 2021 · 8 min · machinelearning 🔥
System Design for Recommendations and Search
Breaking it into offline vs. online environments, and candidate retrieval vs. ranking steps.
27 Jun 2021 · 13 min · teardown production engineering recsys 🔥
Search: Query Matching via Lexical, Graph, and Embedding Methods
An overview and comparison of the various approaches, with examples from industry search systems.
25 Apr 2021 · 21 min · teardown machinelearning production 🔥
How to Write Better with The Why, What, How Framework
Three documents I write (one-pager, design doc, after-action review) and how I structure them.
28 Feb 2021 · 10 min · writing engineering productivity 🩷 🔥
Feature Stores: A Hierarchy of Needs
Access, serving, integrity, convenience, autopilot; use what you need.
21 Feb 2021 · 19 min · teardown machinelearning engineering 🔥
Real-time Machine Learning For Recommendations
Why real-time? How have China & US companies built them? How to design & build an MVP?
10 Jan 2021 · 21 min · teardown machinelearning recsys production 🔥
Unpopular Opinion: Data Scientists Should be More End-to-End
Why (and why not) be more end-to-end, how to, and Stitch Fix and Netflix's experience
09 Aug 2020 · 17 min · datascience machinelearning leadership 🔥
Georgia Tech's OMSCS FAQ (based on my experience)
Why OMSCS? How can I get accepted? How much time needed? Did it help your career? And more...
Stop Taking Regular Notes; Use a Zettelkasten Instead
Using a Zettelkasten helps you make connections between notes, improving learning and memory.
05 Apr 2020 · 6 min · writing learning productivity 🔥
My Journey from Psych Grad to Leading Data Science at Lazada
How hard work, many failures, and a bit of luck got me into the field and up the ladder.
Data Science and Agile (What Works, and What Doesn't)
A deeper look into the strengths and weaknesses of Agile in Data Science projects (Part 1 of 2).
26 Jan 2019 · 13 min · agile datascience productivity 🔥
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