Writing · 2017 to 2020 · Updated 2026

Essays and tutorials

24 pieces on machine learning, Google Cloud and Kubeflow, written for practitioners and for the leaders who work with them. Each one was brought up to date in 2026.

Series · 2019 · 8 parts

Kubeflow for Poets

A guide to containerizing the machine learning production pipeline. It starts with microservices and Docker and ends with an end-to-end solution on Kubeflow Pipelines.

Start the series

01Kubeflow for Poets: A Guide to Containerization of the Machine Learning Production Pipeline
02Introduction
03Microservices Architecture
04Docker
05Kubernetes
06Kubeflow
07Kubeflow Pipelines
08Deploying an End-to-End Machine Learning Solution on Kubeflow Pipelines

2020

Cloud Run: Google Cloud Text-to-Speech API16 Mar

2019

Building a Language Toxicity Classification Model on Google Cloud24 Sept
Cloud Run: Dataset Summaries via HTTP Request6 Jun
Google Cloud AutoML for Medical Image Classification10 May
Working with Keras on Agent Platform Workbench22 Apr
Kubeflow for Poets: A Guide to Containerization of the Machine Learning Production Pipeline27 Mar
Stochastic Processes: Introduction7 Mar

2018

Google NEXT 2018: Google Cloud Platform and the Pythian Partnership25 Jul
An Introduction to Probability and Random Variables for Pattern Recognition 119 Jan
A Comparative Analysis of Amazon SageMaker AI and Google Agent Platform Notebooks17 Jan
Exploring Amazon SageMaker AI14 Jan

2017

Supervised Machine Learning: A Conversational Guide for Executives and Practitioners23 Aug
Demystifying Deep Learning3 Aug
Understanding Machine Learning: An Executive Overview28 Jul
A Gentle Introduction to Google Cloud Platform for Machine Learning Practice21 Jul
Machine Learning on the Cloud: Notes for the Layman12 Jul
Deep Neural Networks for YouTube Recommendations: A Paper Review16 Jan