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 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.
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!
Operators bring a lot of benefits as a way of managing complex software systems in a Kubernetes cluster. In this post, I want to illustrate one in particular: the way that custom resources (and declarative approaches to managing systems in general) enable easy integration with source control and a CI/CD pipeline.
I’ll be using IBM Event Streams as my example here, but the same principles will be true for many Kubernetes Operators, in particular, the open-source Strimzi Kafka Operator that Event Streams is based on.
Installing operators in Red Hat OpenShift from the CLI is much easier with the new kubectl-operator plugin. Here’s an example of how you can use it to install the Event Streams Operator.
Installing operators in OpenShift from the CLI is a little fiddly. It’s possible, but you have to create a bunch of custom resources that aren’t entirely intuitive, like Subscriptions and OperatorGroups.
It’s easy if you’re using the OpenShift Console web UI, as it does this all for you so you don’t need to worry about it. But sometimes you want to do things from the command line. And the new kubectl-operator plugin looks like it’ll make that much simpler.
I had a quick play with it this evening, and it let me get the Event Streams operator running with three commands. (Compare this with the OpenShift Console web UI equivalent in my Event Streams demo video).
I’ve been talking about MirrorMaker 2 this week – the Apache Kafka tool for replicating data across two Kafka clusters. You can use it to make a copy of messages on your Kafka cluster to a remote Kafka cluster running on a different data centre, and keep that copy up to date in the background.
For the discussion we had, I needed to give examples of how you might use MirrorMaker 2, which essentially meant I spent an afternoon drawing pictures. As some of them were a little pretty, I thought I’d tidy them up and share them here.
We went through several different use cases, but I’ll just describe two examples here. Read the rest of this entry »
