I made a cake that looks like a credit card for a machine learning demo. You'll have to read to learn more.
Learn how to call your AutoML models — or really any model — from a container using Cloud Run. In this post you'll learn how to containerize your model prediction code, and use it to scalably serve your ML model.
Progress, it turned out, was a constant battle. Two steps forward followed by one lingering thought that you didn’t deserve to take those steps in the first place. But maybe the progress and doubt didn’t have to be opposing forces. Maybe they could coexist. And maybe, they could even help each other.
Like many people, I’ve been entertaining myself at home by baking a ton. I’ve gotten pretty good at following recipes, but I decided I wanted to take things one step further and understand the science behind what differentiates a cake from a bread or a cookie. I also like machine learning so I thought: what if I could combine it with baking??!
“You are the coolest person I've ever met on a plane,” he messaged me immediately after I sat down. “There's an empty row behind me. Come say hi!”
Anomaly detection can be a good candidate for machine learning, since it is often hard to write a series of rule-based statements to identify outliers in data. In this post I'll look at building a model for fraud detection on financial data. If you're thinking *groan, that sounds boring*, don't go away just yet! Fraud detection addresses some interesting challenges in ML.
Today is my five year Googleversary! I thought I'd take a few minutes to share some things I've learned over the past five years related to developer advocacy, working in tech, and other random thoughts.
Learn how to use custom containers on Cloud AI Platform to train an XGBoost model with automated hyperparameter tuning.
In this post we'll walk through how to interpret an XGBoost model trained on a mortgage dataset using the What-if Tool, SHAP, and Cloud AI Platform.
I collected my own sleep data using an Oura ring and analyzed it with BigQuery.