The absence of comprehensive monitoring tools in today’s complex IT environments introduces significant challenges and risks. Without the ability to oversee the entire stack, organizations may run into an undetected performance issue, leading to potential downtime. According to numerous studies, that can cost between $5,600 and $9,000 per minute. Fortunately, full-stack monitoring emerges as a worthy solution.

Learn more about how IT optimization reduces operating costs, improves efficiency and increases application performance for your business. By working out an optimization initiative, businesses can refine the functionality of their enterprise architectures, ensuring that they’re using resources to their utmost potential. This not only streamlines your automating processes but also bolsters overall performance and business efficiency, allowing you to stay competitive.

In my last blog post on feature extraction, I mentioned something called the bag-of-words (BoW) technique. I decided to write a little bit more on that, mostly just because I think it’s a funny label for something so technical.

In a previous blog post, which was a glossary of terms related to artificial intelligence, I included this brief definition of "feature extraction": Let’s go a bit deeper on that. In the ever-expanding landscape of machine learning, feature extraction stands out as a crucial technique for enhancing the performance of models and uncovering valuable insights from complex datasets.

Effective capacity planning and management are fundamental to maintaining a robust IT infrastructure, helping teams optimize available resources to meet performance needs. In this guide, we’ll walk you through everything you need to know about these invaluable processes to ensure your organization’s IT infrastructure is prepared for current and future demands.

As organizations increasingly depend on distributed system architectures to provide modern applications and microservices, their legacy monitoring tools struggle to keep pace. These outdated systems are often based on predictable failures, but when an unforeseen performance issue occurs, it can lead to outages and unplanned downtime that impacts your customers and your business.

Machine learning is a powerful tool that enables computers to learn from data and make predictions or decisions without being explicitly programmed. Two fundamental approaches to machine learning are supervised and unsupervised learning. In this blog post, we'll explore the key differences between these two approaches, along with examples of their applications.

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.

Applications serve as the backbone of countless operations, driving productivity, customer experience and business success. Tracking and managing their performance is therefore critical to maintain continuity and efficiency, enabling IT teams to proactively identify and resolve issues before they lead to downtime and potential revenue loss. That’s where application performance monitoring (APM) comes in.

In today's rapidly evolving landscape of distributed systems and microservices, understanding how applications behave in production environments has become increasingly complex. Traditional monitoring tools often fall short when it comes to providing comprehensive insights into the performance and behavior of these modern architectures.