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ScienceLogic SL1 AIOps Platform Launched

ScienceLogic announced the launch of ScienceLogic SL1, its new, industry-defining AIOps platform.

ScienceLogic SL1 was designed and developed with the specific goal of leveling the playing field between the pace at which Dev can create great digital experiences, and the ability of Ops to make them resilient experiences.

“Ten years ago, we saw enterprises moving to a state of ephemeral operations, motivated by speed and agility. It started at the infrastructure layer but evolved quickly towards DevOps movements emphasizing service development and rapid release. Today’s performance management systems must provide operational insights in real-time derived from a deep understanding of dependency mapping between applications and their underlying infrastructure,” said ScienceLogic CEO, Dave Link.

SL1 understands how mission-critical applications connect to the underlying infrastructure by deriving topology maps, which enable real-time service health views that inform, analyze and act. This context allows businesses to bring meaning across various data silos and generate insights that drive automated actions like never before.

ScienceLogic SL1 Core Capabilities

- See – Automated real-time discovery inclusive of applications and infrastructure across IT silos and multi-cloud environments

- Contextualize - Automated topology maps to establish real-time relationships between disparate data sets bringing context to data

- Act - Automated issue discovery and subsequent remediation across a diverse range of technologies including CMDB (Configuration Management Database), DevOps and APM (Application Performance Management)

SL1 Technologies:

- PowerMap – If context is king for AIOps, PowerMap is what provides that context. PowerMap creates a multi-dimensional topological map of dependencies in real-time between all components and across all layers of technology - providing immediate context to the underlying raw data. This becomes a crucial input source for Machine Learning engines to provide actionable insights and automations.

- PowerSync – If automation is the endgame of AIOps, PowerSync is the engine that powers it. By providing a universal communication bridge to get data in, share data out and keep data synchronized, PowerSync breaks down the data silos that currently limit automation. It provides multi-directional and intelligent data transformations asynchronously and concurrently among any number of platforms. PowerSync completes the story with a powerful automation engine to enact change to the environment.

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

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 ...

ScienceLogic SL1 AIOps Platform Launched

ScienceLogic announced the launch of ScienceLogic SL1, its new, industry-defining AIOps platform.

ScienceLogic SL1 was designed and developed with the specific goal of leveling the playing field between the pace at which Dev can create great digital experiences, and the ability of Ops to make them resilient experiences.

“Ten years ago, we saw enterprises moving to a state of ephemeral operations, motivated by speed and agility. It started at the infrastructure layer but evolved quickly towards DevOps movements emphasizing service development and rapid release. Today’s performance management systems must provide operational insights in real-time derived from a deep understanding of dependency mapping between applications and their underlying infrastructure,” said ScienceLogic CEO, Dave Link.

SL1 understands how mission-critical applications connect to the underlying infrastructure by deriving topology maps, which enable real-time service health views that inform, analyze and act. This context allows businesses to bring meaning across various data silos and generate insights that drive automated actions like never before.

ScienceLogic SL1 Core Capabilities

- See – Automated real-time discovery inclusive of applications and infrastructure across IT silos and multi-cloud environments

- Contextualize - Automated topology maps to establish real-time relationships between disparate data sets bringing context to data

- Act - Automated issue discovery and subsequent remediation across a diverse range of technologies including CMDB (Configuration Management Database), DevOps and APM (Application Performance Management)

SL1 Technologies:

- PowerMap – If context is king for AIOps, PowerMap is what provides that context. PowerMap creates a multi-dimensional topological map of dependencies in real-time between all components and across all layers of technology - providing immediate context to the underlying raw data. This becomes a crucial input source for Machine Learning engines to provide actionable insights and automations.

- PowerSync – If automation is the endgame of AIOps, PowerSync is the engine that powers it. By providing a universal communication bridge to get data in, share data out and keep data synchronized, PowerSync breaks down the data silos that currently limit automation. It provides multi-directional and intelligent data transformations asynchronously and concurrently among any number of platforms. PowerSync completes the story with a powerful automation engine to enact change to the environment.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

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 ...