Archive for November, 2020

Developer Guide: Machine Learning for Kids

Friday, November 27th, 2020

A run-through of the guide.

In this video, I go from zero to a running Machine Learning for Kids website (including installing all the necessary dependencies and building the site from source).

Describing Kafka with AsyncAPI

Friday, November 27th, 2020

In this post, I want to describe how to use AsyncAPI to document how you’re using Apache Kafka. There are already great AsyncAPI “Getting Started” guides, but it supports a variety of protocols, and I haven’t found an introduction written specifically from the perspective of a Kafka user.

I’ll start with a description of what AsyncAPI is.

“an open source initiative … goal is to make working with Event-Driven Architectures as easy as it is to work with REST APIs … from documentation to code generation, from discovery to event management”

The most obvious initial aspect is that it is a way to document how you’re using Kafka topics, but the impact is broader than that: a consistent approach to documentation enables an ecosystem that includes things like automated code generation and discovery.


Running TensorFlow models in Scratch

Thursday, November 19th, 2020

I gave a short presentation today to explain how you can use TensorFlow machine learning models in the student block-based coding platform, Scratch.

This post has the recording of my presentation, and I’ve put some notes (all the stuff I meant to say but forgot!) and screenshots below.

recording at

I demonstrated three things:

  1. Using your own TensorFlow models in Scratch
  2. Using pretrained models in Scratch
  3. Creating TensorFlow models in Teachable Machine and using them in Scratch


Using TensorFlow.js for training image classifiers

Sunday, November 15th, 2020

Machine Learning for Kids lets students train their own machine learning models in a simplified child-friendly training tool. A variety of project types are supported (such as classifying text, images, numeric data, sound recordings, etc.). Under the covers, machine learning models they train are created and hosted using IBM Watson cloud services, such as Watson Assistant and Watson Visual Recognition

I’m currently investigating image projects being created and hosted in the browser, without using Watson cloud API calls.