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
blog
2020 might have been a bad year outside, but it was a good year for my blog. I wrote 62 posts, almost as many as in the previous 3 years combined (63). Part of it was due to more time because of Covid, part of it was because of the
phone
Yesterday I enjoyed the Australian Șuncuiuș Christmas weather while doing another Via Ferrata trail. It was much harder than the one I did last year. But as I finished the vertical ascent that is seen in the top picture, my phone slipped from my pocket, and fell about 15m. I
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
consulting
In November I quit my job as the tech lead of the machine learning team at Archive360. Starting December 1st I am looking to provide my services as a machine learning consultant.
networking
I want to share about an awesome piece of technology that I started using recently: Tailscale. It's a sort of VPN, meaning it sets up a private network for you, but only between your devices, without a central server. It does so without you having to open any