personal
It's hard to believe it's been more than half an year since I started my own company and became an independent machine learning consultant. It's been a very interesting ride. There have been plenty of moments where the predominant feeling was "what now?
learning
In programming there are so called design patterns, which are basically commonly repeated pieces of code that occur often enough that people thought it would be helpful to give them a name so that’s it’s easier to talk about them. One example is the iterator pattern, which is
machine learning
As much as machine learning developers like to think that once they've got a good enough model, the job is done, it's not quite so. The first couple of weeks after deployment are critical. Is the model really as good as offline tests said they are?
machine learning
One of the most impressive/controversial papers from 2020 was GPT-3 from OpenAI. It's nothing particularly new, it's mostly a bigger version of GPT-2, which came out in 2019. It's a much bigger version, being by far the largest machine learning model at the
machine learning
So the ML engineer presented the model to the business stakeholders and they agreed that it performed well enough on the key metrics in testing that it's time to deploy it to production. So now we have to make sure the models run reliably in production. We have
machine learning
So we finally got our data and we can get to machine learning. Without the data, there is no machine learning, there is at best human learning, where somebody tries to write an algorithm by hand to do the task at hand. This is the part that most people who
machine learning
So we've decided what metrics we want to track for our machine learning project. Because ML needs data, we need to get it. In some cases we get lucky and we already have it. Maybe we want to predict the failure of pieces of equipment in a factory.
machine learning
We saw that machine learning algorithms process large amounts of data to find patterns. But how exactly do they do that? The first step in a machine learning project is establishing metrics. What exactly do we want to do and how do we know we're doing it well?
machine learning
Yesterday we saw that machine learning is behind some successful products and it does have the potential to bring many more changes to our life. So what is it? Well, the textbook definition is that it's the building of algorithms that can perform tasks they were not explicitly
machine learning
Yesterday I wrote how AI made big promises in the past but it failed to deliver, but that now it's different. What's changed? Well, now we have several products that work well with machine learning. My favorite example is Google Photos, Synology Moments and PhotoPrism. They
machine learning
Machine learning is everywhere these days. Mostly in newspapers, but it's seeping into many real life, actual use cases. But what is it actually? If you read only articles on TechCrunch, Forbes, Business Insider or even MIT Technology Review, you'd think it's something that
machine learning
A couple of years ago I was working on a calendar application, on the machine learning team, to make it smarter. We had many great ideas, one of them being that once you indicated you wanted to meet with a group of people, the app would automatically suggest you a