Ready to unlock the power of machine learning to accelerate diagnostics and activate AI-powered root-cause analysis? Here's how to get started.

If you happen to be running multiple clusters, each with a large number of services, you’ll find that it’s rather impractical to use static alerts, such as “number of pods < X” or “ingress requests > Y”, or to simply measure the number of HTTP errors. Values fluctuate for every region, data center, cluster, etc. It’s difficult to manually adjust alerts and, when not done properly, you either get way too many false-positives or you could miss a key event.

There used to be a distinct, technical separation between terms such as AI and machine learning (ML) – but only while these technologies remained largely theoretical. As soon as they became practical in the real world, and then commodifiable into products, the marketers stepped in. Widespread overuse of the terms AI/ML in marketing have managed to thoroughly confuse the meanings of these words.

You can use our API to trigger an on demand run of both the uptime check and the broken links checker. If you add this to, say, your deploy script, you can have near-instant validation that your deploy succeeded and didn't break any links & pages. Our API allows you to trigger an on demand run for every check we do. But, it's an API - so it requires a set of IDs. First, let's find the different checks your site has.

IoT, or the Internet of Things, has made its way into every corner of our lives. Once upon a time, the idea of an inescapable internet may have seemed like a far-off dream. Today, it’s our reality. Internet connected devices are everywhere—from our fitness trackers to our vehicles and appliances. These devices track our sleep patterns, enable us to set our coffee machines remotely, and find our pets after they have wandered off, among countless other tasks.

Software has eaten the world and every company today is a software company. This is because every company today is more and more serving its customers digitally. That service can be a spectrum, such as offering traditional physical products and services through digital channels on one end to offering entirely new digital products on the other end. Regardless of where on the spectrum a company is, it does not change the fact that its primary interface with its customers has become its software.

Every year, more and more website traffic is mobile-driven. Statistically speaking, you’re far more likely to be reading this article now on a mobile device rather than on desktop. Indeed, mobile usage goes far beyond website traffic, as virtually every action we take in modern life, from ordering a takeaway to sharing a photo with friends, takes place on a mobile device.

Machine Learning (ML) algorithms are designed to automatically build mathematical models using sample data to make decisions. Rather than use specific instructions, they rely on patterns and inference instead. And the business applications abound. In recent years, companies such Google and Facebook have found ways to use ML to utilize the massive amounts of data they have for more profit.

Ok, not completely true. I also use Stripe, Github, AWS, Heroku, Ghost, AppOptics, Intercom and Mailchimp. And some bookkeeping and tax tools. Don't forget the code editors! But! When I plan my day, I spend most of my time in Trello and Apple's stock Reminders app. I also use Numbers once a week or so. This post is turning out to be click bait. Read on, it's really not. In former jobs I went through probably every project management and productivity tool out there.