In the digital age, uptime monitoring has become a cornerstone of business operations, ensuring websites and servers are always accessible to users. It's not just about keeping the lights on; it's about preserving reputation, ensuring customer satisfaction, and minimizing revenue loss. Enter Artificial Intelligence (AI), a game-changer in the way we approach uptime monitoring.

I wrote a previous blog post, "AI Explainer: What's Our Vector, Victor?," to scratch the surface on vector databases, which play a crucial role in supporting applications in machine learning, information retrieval and similarity search across diverse domains. From that blog arose the topic of embeddings, which I addressed in a subsequent post, "AI Explainer: Demystifying Embeddings." In explaining embeddings, the notion of continuous space was presented, which is the topic of this blog.

Let's cut through the AI fantasy. You've read the headlines: artificial intelligence is the new electricity, the magic wand transforming businesses overnight. But in my experience working with many clients with InvGate, I've gained some valuable insights. Turns out it’s not all smoke and mirrors. Let's dive into the real story of AI in business, far from the echo chamber of Silicon Valley buzzwords and closer to the tangible reality of everyday corporate life.

In the log aggregation game, the biggest difficulty you face can be setting up parsing rules for your logs. To qualify this statement: simply getting log files into Graylog is easy. Graylog also has out-of-the-box parsing of a wide variety of common log sources, so if your logs fall into one of the many categories of log for which there is either a dedicated Input; a dedicated Illuminate component; or that uses a defined Syslog format; then yes, parsing logs is also easy.

Modern AI applications are having a dramatic impact on our industry, but there are still certain hurdles when it comes to bringing ML models to production. The process of building ML models is so complex and time-intensive that many data scientists still struggle to turn concepts into production-ready models. Bridging the gap between MLOps and DevSecOps workflows is key to streamlining this process.