Operations | Monitoring | ITSM | DevOps | Cloud

While AI seems to be the topic of the moment, especially in the tech industry, the need to make it happen in a reliable way is becoming more obvious. MLOps, as a practice, finds itself in a place where it needs to keep growing and remain relevant in view of the latest trends. Solutions like ChatGPT or MidJourney dominated internet chatter last year, but the main question is…What do we foresee in the MLOps space this year and where is the community of MLOps practitioners focusing their energy?

I have stopped using my Linux distro’s package manager, and you should, too. Maybe I should clarify that. I don’t install software with my distro’s package manager any more. I still upgrade my system. I became influenced by a few different factors. Top among these is something required in certain industries called a change advisory board or committee.

Baking a delicious pizza in a wood-fired oven requires a combination of skill, experience and the right tools. The same is true for achieving optimal observability in a Kubernetes environment. In this post, we'll explore some of the lessons learned from baking pizza in a wood-fired oven and apply them to the world of Kubernetes observability.

When you are designing and building applications, you should consider how to monitor them once they become live. You do not want to be blindsided by errors and degrading performances as you operate them. When your applications fail to provide optimal performance, it can broadly impact your business. Engineers will often be distracted to investigate and fix the issues. Customers will complain. It can eventually hit your bottom line.