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Zenoss Launches Advanced Anomaly Detection

Zenoss announced a major update to existing anomaly detection capabilities for preventing outages in modern IT environments.

The enhancements have enabled Zenoss Cloud to yield an 18% improvement in precision making predictions about anomalous events that are precursors to IT service disruptions.

The anomaly detection enhancements have adapted a modern neural network algorithm from Google Cloud and operationalized it for IT environments. The platform also leverages Vertex AI, a managed machine learning (ML) platform from Google Cloud that accelerates the deployment and maintenance of AI models. Vertex AI requires nearly 80% fewer lines of code to train a model versus competitive platforms, enabling data scientists and ML engineers to efficiently build and manage ML projects throughout the entire development life cycle.

The Zenoss Cloud AIOps platform helps to eliminate the need for customer organizations to employ teams of data scientists, who currently grapple with the challenge of manually piecing together homegrown ML point solutions — creating a lag time in model development and experimentation, resulting in very few models making it into production.

The advanced anomaly detection capabilities deliver:

- Predictive precision improvement of 18%

- An actions framework that notifies downstream systems of anomalous events

- Intelligent, real-time dashboard views that surface top entities exhibiting anomalous behavior based on related metrics that are monitored by machine learning algorithms

- Out-of-box and configurable policies

- At-a-glance view of anomalous entities, root-cause analysis and dependencies

“This is another big step in achieving the goal of a lights-out data center, where the infrastructure is self-healing and doesn’t require humans to manage it,” said Ani Gujrathi, CTO at Zenoss. “This launch continues to advance our AI/ML capabilities and leadership in AIOps with leading-edge technologies to reduce the risks involved with organizations modernizing their environments and moving at the speed of business.”

Zenoss Cloud is an AI-driven full-stack monitoring platform that collects all machine data, uniquely enabling the emergence of context for preventing service disruptions in complex, modern IT environments. Zenoss Cloud leverages some of the most powerful machine learning capabilities and real-time analytics of streaming data to deliver AIOps, giving companies the ability to scale and adapt to the changing needs of their businesses.

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Zenoss Launches Advanced Anomaly Detection

Zenoss announced a major update to existing anomaly detection capabilities for preventing outages in modern IT environments.

The enhancements have enabled Zenoss Cloud to yield an 18% improvement in precision making predictions about anomalous events that are precursors to IT service disruptions.

The anomaly detection enhancements have adapted a modern neural network algorithm from Google Cloud and operationalized it for IT environments. The platform also leverages Vertex AI, a managed machine learning (ML) platform from Google Cloud that accelerates the deployment and maintenance of AI models. Vertex AI requires nearly 80% fewer lines of code to train a model versus competitive platforms, enabling data scientists and ML engineers to efficiently build and manage ML projects throughout the entire development life cycle.

The Zenoss Cloud AIOps platform helps to eliminate the need for customer organizations to employ teams of data scientists, who currently grapple with the challenge of manually piecing together homegrown ML point solutions — creating a lag time in model development and experimentation, resulting in very few models making it into production.

The advanced anomaly detection capabilities deliver:

- Predictive precision improvement of 18%

- An actions framework that notifies downstream systems of anomalous events

- Intelligent, real-time dashboard views that surface top entities exhibiting anomalous behavior based on related metrics that are monitored by machine learning algorithms

- Out-of-box and configurable policies

- At-a-glance view of anomalous entities, root-cause analysis and dependencies

“This is another big step in achieving the goal of a lights-out data center, where the infrastructure is self-healing and doesn’t require humans to manage it,” said Ani Gujrathi, CTO at Zenoss. “This launch continues to advance our AI/ML capabilities and leadership in AIOps with leading-edge technologies to reduce the risks involved with organizations modernizing their environments and moving at the speed of business.”

Zenoss Cloud is an AI-driven full-stack monitoring platform that collects all machine data, uniquely enabling the emergence of context for preventing service disruptions in complex, modern IT environments. Zenoss Cloud leverages some of the most powerful machine learning capabilities and real-time analytics of streaming data to deliver AIOps, giving companies the ability to scale and adapt to the changing needs of their businesses.

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

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