Operations | Monitoring | ITSM | DevOps | Cloud

MLOps pipelines are a set of steps that automate the process of creating and maintaining AI/ML models. In other words, Data Scientists create multiple notebooks while building their experiments, and naturally the next step is a transition from experiments to production-ready code. The best way to do this is to build an effective MLOps pipeline. What’s the alternative, I hear you ask? Well, each time you want to create a model, you run your notebooks manually.

Aiven's OSPO is now one year old. Find out what OS Engineering Manager Josep Prat and his team have learned about setting up and running the function.

I was intrigued by two recent IoT related survey and reports. They emphasize use cases about efficiencies and return on investments but, surprisingly, there is no mention of people-safety nor productivity. I think it is imperative to place people at the heart of the IoT universe. Today, people related use cases have been greatly understated. We will be doing a great injustice if we continue to ignore the human angle.

Debugging JavaScript is an inevitable part of web development, and not the nicest one. Debugging jobs always seem to pop up when you’re already buried under piles of work, and a teammate pings you about an issue that was overlooked in testing and has been causing frustrations since your last release. That’s why it helps to be prepared for that eventuality, and equipped with the developer tools to help you debug faster.

Refereeing decisions at the World Cup have been debated decades later. From whether the ball crossed the line in the final in 1966, through Diego Maradona’s “Hand of God” two decades later, to some of the decisions made by the video assistant referee at Russia 2018, any perceived mistake by the referee will be scrutinized by fans years later. Referees need all the help they can get, and they could be about to be given a hand from artificial intelligence.

Digital businesses are making a radical change in the way they build and deliver software. Gone are the days of apps that rely solely on in-house tools. Rather, today’s apps are increasingly dependent on external APIs and third-party app providers (which, in turn, are reliant on other APIs and apps). While this type of modularity allows for product flexibility and rapid development, it can be difficult to address any issues that arise.