Tag

#MLOps

June 2, 20265 min read

An adversarial persona suite: how I attack my own chatbot

In short: I keep a file of attacks on my own chatbot, each one tied to a written rule, and run them through the real conversation graph against a live model. A broken hard rule fails the run with its…

January 27, 20264 min read

What the buyer actually bought

In short: I sold my startup and its technology, and I do not think the buyer was paying for clever code. They were paying for the parts that keep the product working without me: the pipeline that…

July 15, 20255 min read

What I look for when hiring an ML engineer

In short: I look for someone who checks the data before the model, gets suspicious when a result looks too good, has shipped at least one thing end to end, and can explain it to someone who isn't an…

May 20, 20255 min read

Most AI never leaves the notebook

In short: most models die between the demo and the first real user, and rarely because the model was bad. They die because nobody owns them, the real data looks different, the metric doesn't match a…

April 8, 20254 min read

Monitoring a model that listens to patients

In short: for a voice model in healthcare, the true labels arrive late or never, so you can't watch accuracy. You watch the recordings coming in, the scores going out, and the plumbing in between, and…

March 25, 20255 min read

Cloud Run or EC2: how I chose for a model that listens

In short: if the service takes a finished recording, scores it and answers, Cloud Run is usually the simpler and cheaper home. If it holds a live audio stream open, needs a GPU, or has to stay warm…

December 31, 20244 min read

The boring checklist that saved every project

In short: before I train anything I want three things: a record of where every piece of data came from, a dumb baseline to beat, and one number the people paying for the work actually care about. None…

December 3, 20244 min read

Running AI on your own machines

In short: when data is not allowed to leave the building, the smallest useful setup is two machines on the same network: one that asks, one with the GPU that answers. It takes about thirty lines of…

September 10, 20244 min read

Experiment tracking I actually keep using

In short: I use DVC to version data, MLflow to record runs and models, and Weights and Biases when I want to watch a long training live or share charts. What makes them useful is one habit: every run…

July 30, 20245 min read

Federated learning: the model was the easy part

In short: when sites can't share their data, the model code is the smallest part of the work. Most of the effort goes into making the data mean the same thing everywhere, coping with sites that drop…