Paradigm Shift

Early in my career, I followed a Python programmer named Gary Bernhardt.

For years, I was that engineer who obsessed over the perfect abstraction, the cleanest architecture, the most elegant solution.

It turns out, you can build a surprisingly powerful keyword search, known as BM25, with just a handful of SQL functions.

I set out to build a BERT like encoder from scratch this year.

After ElixirConf I found myself increasingly immersed in the world of fine-tuning.

On November 1st I joined the first RAG cohort from MLOps.Community and went hard for 8 weeks building a proof of concept that combined everything from embedding models, prompting, and retrieval to ranking with a cross encoder to support hybrid search.

In 2023 I spent a lot of time at the intersection of software engineering and machine learning hoping to uncover the next great opportunity for automation.

Fine-tuning with Bumblebee is great but large models such as Mistral 7B demand over 100GB of vRAM to fine tune with full precision.

Yesterday I upgraded to the latest version of bumblebee only to learn EXLA took on the latest XLA which dropped support for CUDA 11.2.

A few months back I started a deep dive into machine learning.

Throughout my career I've had the opportunity to blend, extend and adapt well known engineering practices to match the accelerated demands put on product teams.

Last week I started a new role and ahead of this new adventure I read A Philosophy of Software Design with the specific purpose of gifting the book and a summary of it to each of my engineers.

This week I started a new role and for the first time I've put team health at the forefront by re-reading Crucial Conversations with the specific purpose of gifting the book and a summary of it to each of my engineers.

This week I finished an upgrade from Elixir 1.10 to 1.11.3 and more notably Phoenix 1.4 to 1.5.7.

This year I read Shape Up, Team Topologies and a slightly more academic book on the topic of lean called The Principles of Product Development Flow in an effort to learn more about delivery engineering.