Zenoss Chief Product Officer Trent Fitz was recently featured on the “Future Tech and Foresight” podcast with host Marc Verbenkov. On the podcast, Trent unraveled the complexities and indispensable role of AI in modern IT operations, which is reshaping the landscape of technology across industries.

Network monitoring is an essential pillar of maintaining a healthy IT infrastructure. Without an effective network monitoring system in place, your organization could be losing out on better performance, greater efficiency and increased cost savings. In this guide, we'll walk you through everything you need to know about network monitoring to understand what it is and why it’s critical for your operations.

In today's tech landscape, businesses are undergoing a significant shift in how they manage their IT infrastructure. Cloud computing has been a huge force in this transformation, leading to a blend of on-prem and cloud-based services known as hybrid IT. With a hybrid IT infrastructure, you can take advantage of both the control and security of on-prem solutions, as well as the scalability and flexibility of cloud architecture. But is hybrid IT right for your business?

In a previous blog post, entitled "What's Our Vector, Victor?," I went through the basics of vector databases. That post explained how vector databases are used by large language models, and one of the concepts included was this brief explanation of embeddings: So, let's dig in a little more on this. Embeddings, in the context of vector databases, refer to vector representations of data points or entities within the database.

The reference in the title is from the 1980 film “Airplane.” If you recognized it, I’m sorry to inform you that you’re getting old (like me). Let’s talk about vector databases. A vector database is a type of database that stores and organizes data in the form of vectors, which are mathematical representations of objects or entities in a multidimensional space.

Last week we announced the release of new capabilities to proactively identify and address website and web application issues that may impact user experience or the functionality of the applications. The capability, called site availability monitoring (SAM), is immediately available to all customers.

Computers understand numbers. So, how do large language models (LLMs) mimic human speech? Do LLMs like ChatGPT actually understand words? The short answer is no. LLMs process and represent words using numerical embeddings. These numerical representations enable the model to perform computations, make predictions and generate text. However, it's essential to clarify that the model doesn't possess a true understanding of words in the way humans do. Here's a breakdown of the process.

ChatGPT, developed by OpenAI and launched in November 2022, isn’t the only large language model that has received lots of attention lately, but it’s by far the most widely known. A previous blog post that listed a glossary of AI terms included this brief definition: You may have read over the past year that GPT-4 (the paid version of ChatGPT) has been able to pass many difficult exams. Here are just a few.

Zenoss Chief Product Officer Trent Fitz was recently featured on the "Tech Talks Daily" podcast, hosted by Neil Hughes. Trent is a pioneer in the realm of AIOps with over two decades of experience in artificial intelligence, cloud computing and cybersecurity. His expertise in cloud computing has significantly influenced his work with AIOps, especially in managing the increasing complexity of cloud-based infrastructure.

In a previous blog post, which was a glossary of terms related to artificial intelligence, I included this brief definition of "neural networks": Let’s go a bit deeper on that. Neural networks are a class of artificial intelligence (AI) and machine learning models inspired by the structure and functioning of the human brain. They are a subset of AI techniques that have gained significant popularity due to their ability to learn and make decisions from data.