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ScienceLogic Announces Hollywood Release of SL1 Platform

ScienceLogic introduced a combination of machine learning and automation capabilities with the release of Hollywood, a major update to its flagship SL1 platform.

With this release, ScienceLogic SL1 delivers a more intuitive approach for converting AI insights into automated action. AI/Machine Learning (ML) techniques are used to learn from customer IT environments and provide human-friendly insights for up to 10x faster issue resolution. Generative AI can review information collected from across hybrid cloud environments and create easy-to-understand analysis that all levels of IT can use to take action. AI also recommends automation workflows to run, or can be set to run them automatically, deflecting issues from human operators and allowing IT – and by extension – the business to run more efficiently.

AI and automation capabilities are built directly into an improved, more intuitive SL1 user interface, which displays IT operational information at the business service level for rapid understanding of business impact and improved Mean Time to Repair (MTTR). Combined with integrations to Slack, WebEx, and other collaboration systems, SL1 sets the bar for engaging and coordinating action across multiple teams, ensuring issues can be quickly resolved.

“With the launch of Hollywood, ScienceLogic customers will realize the first of many benefits of our 2022 acquisition of Zebrium. We have fully integrated its ML-driven root cause analysis capabilities into the SL1 platform and coupled with our automation capabilities to deliver support for truly self-healing services across the IT environment,” says Michael Nappi, CPO at ScienceLogic. “This is advanced AIOps, smarter, and easier - and it’s only the beginning. We’re excited about the potential for even more advanced analytical capabilities to fundamentally transform IT operations in the year ahead.”

The ScienceLogic Hollywood release includes:

- AI/ML- Driven Root Cause Analysis (RCA): AI provides rapid RCA in an intuitive natural language format to radically reduce time the time to isolate, identify and resolve service impacting issues. The ML model is easy to provision and train - and provides accurate and actionable insights without human supervision - so your IT team can focus on delivering new services to the business.

- Modern, Unified User Experience: a more modern and intuitive interface allows customers to rapidly understand the wealth of services delivered to the business and the relationship of infrastructure and application components to those services.

- SL1 Low / No Code Toolkit: A comprehensive SL1 toolkit enables DevOps teams to quickly build or customize PowerPacks - monitoring templates that connect unique devices/services/applications and describe how SL1 should monitor and visualize those components in the platform.

- New Workflow Integrations for Faster Issue Response: More than ever, IT teams communicate via collaboration tools, so with this release adds Slack and WebEx to existing integrations like Microsoft Teams to accelerate IT team’s efficiency and productivity.

“Hollywood demonstrates ScienceLogic’s commitment to empowering the IT function and propelling enterprises forward through our SL1 platform. With this update, we’re enabling our customers to truly leverage intelligence to achieve unprecedented levels of efficiency and agility with their IT operations,” said Tina McNulty, ScienceLogic CMO. “It’s an exciting moment to be able to showcase ScienceLogic’s commitment to overcoming the increasing complexities of the IT landscape. As IT infrastructure continues to grow across cloud, hybrid, and edge environments, AIOps becomes mandatory. With Hollywood, SL1 has created an AIOps foundation for the future.”

Hollywood will be available to early access program customers with general availability of the release planned for early 2024.

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ScienceLogic Announces Hollywood Release of SL1 Platform

ScienceLogic introduced a combination of machine learning and automation capabilities with the release of Hollywood, a major update to its flagship SL1 platform.

With this release, ScienceLogic SL1 delivers a more intuitive approach for converting AI insights into automated action. AI/Machine Learning (ML) techniques are used to learn from customer IT environments and provide human-friendly insights for up to 10x faster issue resolution. Generative AI can review information collected from across hybrid cloud environments and create easy-to-understand analysis that all levels of IT can use to take action. AI also recommends automation workflows to run, or can be set to run them automatically, deflecting issues from human operators and allowing IT – and by extension – the business to run more efficiently.

AI and automation capabilities are built directly into an improved, more intuitive SL1 user interface, which displays IT operational information at the business service level for rapid understanding of business impact and improved Mean Time to Repair (MTTR). Combined with integrations to Slack, WebEx, and other collaboration systems, SL1 sets the bar for engaging and coordinating action across multiple teams, ensuring issues can be quickly resolved.

“With the launch of Hollywood, ScienceLogic customers will realize the first of many benefits of our 2022 acquisition of Zebrium. We have fully integrated its ML-driven root cause analysis capabilities into the SL1 platform and coupled with our automation capabilities to deliver support for truly self-healing services across the IT environment,” says Michael Nappi, CPO at ScienceLogic. “This is advanced AIOps, smarter, and easier - and it’s only the beginning. We’re excited about the potential for even more advanced analytical capabilities to fundamentally transform IT operations in the year ahead.”

The ScienceLogic Hollywood release includes:

- AI/ML- Driven Root Cause Analysis (RCA): AI provides rapid RCA in an intuitive natural language format to radically reduce time the time to isolate, identify and resolve service impacting issues. The ML model is easy to provision and train - and provides accurate and actionable insights without human supervision - so your IT team can focus on delivering new services to the business.

- Modern, Unified User Experience: a more modern and intuitive interface allows customers to rapidly understand the wealth of services delivered to the business and the relationship of infrastructure and application components to those services.

- SL1 Low / No Code Toolkit: A comprehensive SL1 toolkit enables DevOps teams to quickly build or customize PowerPacks - monitoring templates that connect unique devices/services/applications and describe how SL1 should monitor and visualize those components in the platform.

- New Workflow Integrations for Faster Issue Response: More than ever, IT teams communicate via collaboration tools, so with this release adds Slack and WebEx to existing integrations like Microsoft Teams to accelerate IT team’s efficiency and productivity.

“Hollywood demonstrates ScienceLogic’s commitment to empowering the IT function and propelling enterprises forward through our SL1 platform. With this update, we’re enabling our customers to truly leverage intelligence to achieve unprecedented levels of efficiency and agility with their IT operations,” said Tina McNulty, ScienceLogic CMO. “It’s an exciting moment to be able to showcase ScienceLogic’s commitment to overcoming the increasing complexities of the IT landscape. As IT infrastructure continues to grow across cloud, hybrid, and edge environments, AIOps becomes mandatory. With Hollywood, SL1 has created an AIOps foundation for the future.”

Hollywood will be available to early access program customers with general availability of the release planned for early 2024.

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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 ...