Operations | Monitoring | ITSM | DevOps | Cloud

Open source is one of the key drivers of DevOps. The need for flexibility, speed, and cost-efficiency, is pushing organizations to embrace an open source-first approach when designing and implementing the DevOps lifecycle. Monitoring — the process of gathering telemetry data on the operation of an IT environment to gauge performance and troubleshoot issues — is a perfect example of how open source acts as both a driver and enabler of DevOps methodologies.

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.

Recently I pushed a long overdue feature for Checkly: SSL for customers' public dashboards. This was kinda, sort of, totally missing when I launched and many customers asked for it. Setting up free SSL turned out to be fairly smooth because of Most principles explained here are totally transferable to whatever stack you are using. There are some pretty important gotcha's though, so let's dive in. Customers of Checkly can create public dashboards and host them on a custom domain.

Many data science discussions focus on model development. But as any data scientist will tell you, this is only a small—and often relatively quick—part of the data science pipeline. An important, but often overlooked, component of model stewardship is monitoring models once they’ve been released to the wild. Here we’ll aim to convince any unbelievers that monitoring deployed models is as important as any other task in the data science workflow.