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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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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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