Yanir Seroussi – AI/ML Success Architect

With the commodification of data scientists, the problem of positioning has become more common: My takeaways from Genevieve Hayes interviewing Jonathan Stark.

An interesting approach to bidding of energy storage assets, showing that training on New York data is transferable to Queensland.

Summarising the work Uri Seroussi and I did to improve Reef Life Survey’s Reef Species of the World app.

One of my many highlights from Helen Czerski’s Blue Machine.

For many use cases, libraries like cartopy are better than the likes of Mapbox and Google Maps.

Video and summary of a talk I gave at DataEngBytes Brisbane on what I learned from doing data engineering as part of every data science role I had.

Replacing ‘artificial intelligence’ with ‘automation’ is a useful trick for cutting through the hype.

It turns out that problems like finding a niche and defining the ideal clients are key to any solo business.

Despite the excitement around large language models, building with machine learning remains an engineering problem with established best practices.

Reflections on publishing on this website: Writing publicly to share thoughts and documentation beats chasing views and likes.

While I found the story of Gumroad interesting, The Minimalist Entrepreneur seems to over-generalise from the founder’s experience.

A summary of the second chapter of Rob Walling’s Start Small, Stay Small, along with my thoughts & reflections.

A summary of the first chapter of Rob Walling’s Start Small, Stay Small, along with my thoughts & reflections.

GitHub publishes an Atom feed, which means you can use any RSS reader to follow commits.

Turns out that the rule of thirds for composing visuals may not be that important.

Some pointers for setting up SSH access with YubiKey on Ubuntu 22.04.

How I added a Today I Learned section to my Hugo site with the PaperMod theme.

Time can be spent doing different activities, but it can’t be stored and saved for later.

Yes, data science projects have suffered from classic software engineering mistakes, but the field is maturing with the rise of new engineering roles.

Exploring the hackability of speed-based coding tests, using CodeSignal’s Industry Coding Framework as a case study.

Bing Chat recently quipped that humans are small language models.

My perspective after a week of using ChatGPT: This is a step change in finding distilled information, and it’s only the beginning.

Reviewing the first three chapters of the book Causal Machine Learning by Robert Osazuwa Ness.

Discussing my recent career move into climate tech as a way of doing more to help mitigate dangerous climate change.

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.

Epidemiologists analyse clinical trials to estimate the intention-to-treat and per-protocol effects.

Overview of a talk I gave at a deep learning course, focusing on AI ethics as the need for humans to think on the context and consequences of applying AI.

My reasons for switching from WordPress.com to Hugo on GitHub + Cloudflare, along with a summary of the solution components and migration process.

Back-dated meta-post that gathers my posts on Automattic blogs into a summary of the work I’ve done with the company.

Sharing remote teamwork insights, my climate & sustainability activism, Reef Life Survey publications, and progress on Automattic’s Experimentation Platform.

Going deeper into correct testing of different methods for bootstrap estimation of confidence intervals.

Being a data scientist can sometimes feel like a race against software commodities that replace interesting work.

Video of a talk I gave on remote data science work at the Data Science Sydney meetup.

Video and summary of a talk I gave at YOW!

Bootstrap sampling has been promoted as an easy way of modelling uncertainty to hackers without much statistical knowledge.

Causal Inference by Miguel Hernán and Jamie Robins is a must-read for anyone interested in the area.

Discussing the pluses and minuses of remote work eighteen months after joining Automattic as a data scientist.

Updating my definition of data science to match changes in the field.

Frequently asked questions by visitors to this site, especially around entering the data science field.

Call for BCRecommender maintainers followed by a decision to shut it down, as I don’t have enough time and Bandcamp now offers recommendations.

I wanted a well-paid data science-y remote job with an established company that offers a good life balance and makes products I care about.

Web tools I built to visualise Reef Life Survey data and assist citizen scientists in underwater visual census work.

There’s a lot of misleading content on the estimation of customer lifetime value.

Video and summary of a talk I gave at the Data Science Sydney meetup, about going beyond the what & how of predictive modelling.

Seven common mistakes to avoid when working with data, such as ignoring uncertainty and confusing observed and unobserved quantities.

It seems like anyone who touches data can call themselves a data scientist, which makes the title useless.

A web tool I built to interpret A/B test results in a Bayesian way, including prior specification, visualisations, and decision rules.

Discussing the need for untested assumptions and temporality in causal inference.

Is artificial/machine intelligence a future threat?

Causality is often overlooked but is of much higher relevance to most data scientists than deep learning.