In a previous blog post, which was a glossary of terms related to artificial intelligence, I included this brief definition of "reinforcement learning": I expect this definition would prompt many to ask, "What rewards can you give a machine learning agent?" A gold star? Praise? No, the short answer is: numerical values. In reinforcement learning, rewards are crucial for training agents to make decisions that maximize their performance in a given environment.

I previously posted a blog that was a glossary of terms related to artificial intelligence. It included this brief definition of "generative AI": I expect for someone learning about AI, it's frustrating to read definitions of terms that include other terms you may not understand. In this case, generative adversarial networks — GANs — is probably a new term for many. This post will explain what GANs are for that reason — and also because they’re super cool.

In a previous blog post, which was a glossary of terms related to artificial intelligence, I included this brief definition of "data preprocessing": It is common for people familiar with these matters to talk about not having clean data. When dealing with AI for whatever your needs are, clean data is crucial for the quality of results. Garbage in, garbage out, as they say. So, let’s dive into what it means to have clean data.

In today's fast-paced digital world, the reliability and performance of IT infrastructure are critical to business success. Monitoring technology plays a pivotal role in ensuring that systems and networks operate seamlessly, and one such technology is Zenoss. This blog provides an in-depth look at Zenoss technology and sheds light on the importance of event-driven architecture in modern monitoring.

I speak with customers and partners pretty much every week about artificial intelligence. The knowledge levels can differ quite dramatically — some are quite AI savvy while others find the jargon bewildering. This is quite understandable as AI is a rapidly evolving field with its own set of specialized terminology. This blog post is purely meant to provide a beginner-friendly reference for some essential AI terms to make it easier to navigate conversations and articles on the topic.

Zenoss Chief Product Officer Trent Fitz recently spoke with Dan Turchin, host of the podcast “AI and the Future of Work,” and shared some insightful perspectives on the evolution of monitoring in the IT industry, the role of AIOps tools, and the challenges of moving to the cloud. They also discussed Trent’s extensive background in computer engineering and his experience driving product innovation and strategy in various technology fields.

As technology advances, the networks required to support it have evolved into complex, interconnected systems that are vital to each organization’s operations. When a network issue causes service interruptions or unexpected downtime, it can have serious consequences not only for service performance but also for the company’s bottom line. Sometimes, all it takes is an overloaded device or a faulty connection, and suddenly everything grinds to a halt.

Data scientists have long been aware of the concept of “garbage in, garbage out” — the idea that the quality of results is a direct indicator of the quality of data. Indeed, much effort has been expended in the pursuit of cleansing data to ensure its accuracy. It then should come as no surprise that AI and machine learning (ML) algorithms are also subject to the same quality standards.

In this video blog post, I delve into the world of Zenoss Smart View, an indispensable tool that has revolutionized the way IT operations personnel approach diagnostic challenges. In today's fast-paced and complex digital landscape, swift problem resolution is paramount. That's precisely where Smart View shines. Smart View is a critical, differentiated tool in Zenoss’ toolkit to identify critical issues with time-sensitive, contextual information.