We’ve been running a virtual event this week to explain the capabilities of IBM’s Cloud Pak for Integration.
One of these is Event Streams, so I gave an overview of the Event Streams Operator.
But what it really reminded me is that I miss going to conferences and tech events. I don’t want to sound ungrateful for what I’m sure has been a huge amount of work for event organisers in the pivot to online events. It’s great that we can still do events at all, and that organisers are still trying out ways to make it interactive, to enable panels and Q&A sessions.
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!
