Google's Rules of Machine Learning still apply in the age of large language models
Despite the excitement around large language models, building with machine learning remains an engineering problem with established best practices.
software engineering
Was data science a failure mode of software engineering?
Yes, data science projects have suffered from classic software engineering mistakes, but the field is maturing with the rise of new engineering roles.
How hackable are automated coding assessments?
Exploring the hackability of speed-based coding tests, using CodeSignal’s Industry Coding Framework as a case study.
Building useful machine learning tools keeps getting easier: A fish ID case study
Lessons learned building a fish ID web app with fast.ai and Streamlit, in an attempt to reduce my fear of missing out on the latest deep learning developments.
My work with Automattic
Back-dated meta-post that gathers my posts on Automattic blogs into a summary of the work I’ve done with the company.
Software commodities are eating interesting data science work
Being a data scientist can sometimes feel like a race against software commodities that replace interesting work. What can one do to remain relevant?
Bootstrapping the right way?
Video and summary of a talk I gave at YOW! Data on bootstrap estimation of confidence intervals.
Hackers beware: Bootstrap sampling may be harmful
Bootstrap sampling has been promoted as an easy way of modelling uncertainty to hackers without much statistical knowledge. But things aren’t that simple.
Exploring and visualising Reef Life Survey data
Web tools I built to visualise Reef Life Survey data and assist citizen scientists in underwater visual census work.
Is Data Scientist a useless job title?
It seems like anyone who touches data can call themselves a data scientist, which makes the title useless. The work they do can still be useful, though.
Migrating a simple web application from MongoDB to Elasticsearch
Migrating BCRecommender from MongoDB to Elasticsearch made it possible to offer a richer search experience to users at a similar cost, among other benefits.
The wonderful world of recommender systems
Giving an overview of the field and common paradigms, and debunking five common myths about recommender systems.
Goodbye, Parse.com
Migrating my web apps away from Parse.com due to reliability issues. Self-hosting is a better solution.
First steps in data science: author-aware sentiment analysis
I became a data scientist by doing a PhD, but the same steps can be followed without a formal education program.
Automating Parse.com bulk data imports
A script for importing data into the Parse backend-as-a-service.
What is data science?
Data science has been a hot term in the past few years. Still, there isn’t a single definition of the field. This post discusses my favourite definition.
Building a recommender system on a shoestring budget (or: BCRecommender part 2 – general system layout)
Iterating on my BCRecommender service with the goal of keeping costs low while providing a valuable music recommendation service.