It was quite a long session, as we wanted it to be a hands-on practical CPD (Continuing Professional Development) workshop rather than just me giving a short talk. In the 90-minute workshop, we made two separate AI projects, which was a chance to see and contrast a few different approaches.
I started with a couple of definitions, then demonstrated a variety of projects that I’ve seen primary school students make, and finally walked people through a hands-on demo so they could try it out for themselves.
I’m working on interactive visualisations for Machine Learning for Kids that explain more of the machine learning models that children create.
Machine Learning for Kids is a platform to teach children about artificial intelligence and machine learning, by giving them a simple tool for training machine learning models, and using that to make projects using tools like Scratch. I’ve described before how I’ve seen children learn a lot about machine learning principles by being able to play and experiment with it.
This post is a simple example of how to use a machine learning model to make predictions on a stream of events on a Kafka topic.
It’s more a quick hack than a polished project, with most of this code hacked together from samples and starter code in a single evening. But it’s a fun demo, and could be a jumping-off point for starting a more serious project.
For the purposes of a demo, I wanted to make a simple example of how to implement this pattern, using:
sensors that are easily and readily available, and
predictions that are easy to understand (and easy to generate labelled training data for)
With that goal in mind, I went with:
for the sensors providing the source of events, I used the accelerometer and gyroscope on my iPhone
for the machine learning model, I used TensorFlow to make a simple bidirectional LSTM
the predictions I’m making are a description of what I’m doing with the phone (e.g. is it in my hand, is it in my pocket, etc.)
I’ve got my phone publishing a live stream of raw sensor readings, and passing that stream through an ML model to give me a live stream of events like “phone has been put on a table”, “phone has been picked up and is in my hand”, or “phone has been put in a pocket while I’m sat down”, etc.
Here is it in action. It’s a bit fiddly to demo, and a little awkward to film putting something in your pocket without filming your lap, so bear with me!
In this post, I want to explain how to get started creating machine learning applications using the data you have on Kafka topics.
I’ve written a sample app, with examples of how you can use Kafka topics as:
a source of training data for creating machine learning models
a source of test data for evaluating machine learning models
an ongoing stream of events to make predictions about using machine learning models
I’ll use this post to explain how it works, and how you can use it as the basis of writing your first ML pipeline using the data on your own Kafka topics.
This week I ran a remote workshop for school teachers about machine learning and artificial intelligence. It was organised with University College London as part of a series of activities they are running to celebrate the CS Expo: 40+ years of UCL Computer Science.
