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
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