Operations | Monitoring | ITSM | DevOps | Cloud

Something critical just went down. Again. So you troubleshoot and find out everything's clean - logs, metrics, nothing seems out of the ordinary. You didn't think to look out the window, right? Let's rewind a couple of hours. The temperature spiked 15 degrees outside, the humidity was at 90% and a storm came out of nowhere. Meanwhile, your edge device is sitting in a box on a pole somewhere; it never stood a chance.

Ever tried applying for a loan, renting an apartment, or just upgrading your phone? At some point, someone asks, "Can you show us proof of income?" It seems straightforward, until you realize things have shifted. It's now 2025. Digital payments, side hustles, freelancing, and even crypto earnings have now made things that used to be simple much more complicated. How do you even begin to explain your income when you've got income coming in from five different sources?

The landscape of entrepreneurship in 2025 looks nothing like it did just a few years ago. With AI tools, low-cost infrastructure, and easier access to global markets, launching a startup has become more attainable than ever. But while the tools have changed, the fundamentals of discovering a strong startup idea remain the same - and arguably more important.

We chose to feature this article because it reflects exactly what OpsMatters stands for: practitioners solving real problems with practical DevOps thinking. When we came across Ashish's detailed breakdown of his experience modernizing a complex ML environment, it stood out for its clarity and actionable insights. We reached out to him to learn more about the work behind this case study, and with his permission, we are sharing it here so the broader community can benefit from these lessons in observability, cost optimization, and real-world DevOps execution.

Emotions are running high about AI technologies. In this 2-parter, I do my best to make a rational case on the reality of AI, and how we can respond to it. This is part one; part two next week. We seem to be struggling to have pragmatic discussions about advancements in Artificial Intelligence. It's hard to hear calmer voices over the detractors and breathless enthusiasts. Today, I want to make a reasoned, evidence-based case for the potential of this technology, glance at present and future applications, and offer some practical examples for implementing AI within an organization.