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By Dallon Robinette
The previous post in this series focused on shared context and why hybrid operations depend on a connected view across cloud, network, and infrastructure. Once that context is in place, the operational benefits become easier to see—especially during incident response, where signal volume and fragmented tooling can slow teams down. Alert noise remains one of the most persistent challenges in hybrid environments. Every layer of the stack can generate its own warnings, anomalies, and service events.
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By Dallon Robinette
AI is moving out of the experimental phase and into the everyday rhythm of work. Teams are no longer using it occasionally for novelty or quick wins, but instead are exploring more robust use cases to investigate issues, answer questions faster, surface context, and help them move through complex workflows with more confidence. That’s the shift that most organizations’ leadership teams have been asking for.
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By Dallon Robinette
The first post in this series explored why traditional observability breaks down in hybrid cloud environments. As infrastructure, applications, and dependencies stretch across on-premises networks and cloud services, isolated monitoring views leave teams with an incomplete understanding of what is happening and why. That challenge raises the next question: what kind of operational model actually works in a hybrid environment?
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By Dallon Robinette
Hybrid cloud has reshaped the way enterprises build, run, and troubleshoot digital services. Applications now stretch across on-premises infrastructure, cloud platforms, regional services, interconnects, and distributed dependencies that change constantly. Operational complexity has expanded with that footprint, yet many observability practices still reflect assumptions from an earlier era of simpler architectures and clearer boundaries. That gap shows up fast during an incident.
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By Dallon Robinette
The discussion around AI in infrastructure and operations has become increasingly model-centric. Teams want to know what model a platform uses, how current it is, how much reasoning capacity it has, and how quickly it can be updated as the model landscape shifts. Those are reasonable questions, but they tend to arrive too early. In production operations, the more consequential question is what happens to the data before any model is asked to interpret it.
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By Bob Slevin
Most IT teams do not suffer from a lack of data. They suffer from the amount of effort required to make sense of it. Every network device, application, cloud service, and infrastructure component generates a constant stream of machine output. Logs capture state changes, failures, retries, warnings, and thousands of other small signals about how systems behave. The problem is that raw logs are hard to use at operational speed.
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By Bob Slevin
Operations teams have lived with the same frustrating tradeoff for years: the data exists, but getting to the right answer often takes too much time and too much expertise. Engineers are expected to know platform-specific query languages, navigate layers of dashboards, and understand exactly where the right visualization lives before they can even begin troubleshooting. That approach can work in smaller environments, but as infrastructure grows more distributed and complex, it becomes a bottleneck.
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By Dallon Robinette
Anyone who has spent time in a NOC knows how quickly a routine issue can turn into a scramble. A user in a branch office reports that a critical application is unavailable. Slack starts lighting up, dashboards begin to fill with warnings, and before long several teams are trying to answer the same basic question at once: what exactly is broken, where is it broken, and who owns the next move?
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By Bob Slevin
For years, IT operations teams have been trapped in a frustrating paradox: the data they need to solve critical issues is right at their fingertips, yet entirely out of reach. Accessing it requires engineers to master complex, platform-specific query languages, dig through endless layers of dashboards, and hunt for the exact visualization that holds the answer. Under the intense pressures of modern speed, scale, and complexity, this rigid model is breaking down.
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By Dallon Robinette
Modern network operations generate an extraordinary amount of telemetry. Metrics, logs, events, topology data, cloud signals, and service context all contribute to a richer picture of system behavior. As environments expand across cloud, data center, edge, and SaaS, the opportunity for operations teams is clear: when that telemetry is unified and understood in context, it becomes a powerful source of resilience, efficiency, and business insight.
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By Selector
What can decades of hands-on operational experience teach us about the future of AI-driven networking? In this episode of Next-Gen Network Heroes, host Bob Slevin sits down with Jeremy Bradberry, Senior Network Engineer at Delaware North, for a conversation that spans everything from legacy manufacturing systems and mainframes to modern AI-assisted network operations. Jeremy shares how his early career working in industrial environments shaped the way he approaches networking today, giving him what he calls an “X-ray vision” into how technology connects directly to business operations.
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By Selector
What happens when decades of operational experience meet modern AI-driven networking? In the latest episode of Next-Gen Network Heroes, Bob Slevin sits down with Jeremy Bradberry, Senior Network Engineer at Delaware North, to explore how network engineers can modernize infrastructure without losing sight of the operational realities behind the technology. Jeremy shares lessons learned from working on legacy manufacturing systems, how AI is helping engineers analyze data and automate workflows faster than ever before, and why strong standards still matter in today’s AI era.
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By Selector
Most people think great network engineers are defined by technical expertise. This episode challenges that idea. Because what Troy McDonald shows is that the real differentiator isn’t just technical skill—it’s the ability to translate complexity into clarity. From military operations to enterprise networks, one lesson keeps showing up.
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By Selector
What does it take to succeed in networking when complexity is constantly increasing, and change never slows down? In this episode of Next-Gen Network Heroes, host Bob Slevin sits down with Troy (David) MacDonald, a network engineer at Blue Origin and former U.S. Army Chief Warrant Officer, to explore a career that spans from infantry beginnings to designing and managing large-scale, mission-critical networks.
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By Selector
What does it take to reinvent network visibility from the ground up? In this episode of Next-Gen Network Heroes, Bob sits down with Liang Chen, Senior Network Architect at Texas Children’s Hospital and creator of a next-generation network traffic analyzer built for real-time, packet-level visibility. Liang shares how he built a platform capable of analyzing traffic at up to 200Gbps with zero packet loss—unlocking deeper network forensics and faster troubleshooting in mission-critical environments.
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By Selector
What happens when deep networking expertise meets low-level programming and a passion for invention? In this episode of Next-Gen Network Heroes, host Bob Slevin sits down with Liang Chen, Senior Network Architect at Texas Children's Hospital and a true innovator in network performance and visibility. With more than 25 years of experience in networking, plus advanced expertise in programming languages like C and Assembly, Liang has built his own next-generation traffic analysis platform from the ground up—designed to provide real-time, packet-level visibility at massive scale.
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By Selector
AI is changing network operations faster than ever. In the latest episode of Next-Gen Network Heroes, Bob sits down with Greg Freeman of Lumen Technologies to talk about what it takes to innovate across one of the world’s largest telecommunications networks. From deterministic workflows to agentic AI, Greg shares how his team is using automation, analytics, and AI to improve network reliability, customer experience, and operational efficiency at scale.
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By Selector
What does it take to lead innovation across one of the world’s largest telecommunications networks? In this episode of Next-Gen Network Heroes, host Bob Slevin sits down with Greg Freeman, Vice President of Network and Customer Transformation at Lumen Technologies, to explore how AI, automation, and curiosity are reshaping the future of network operations.
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By Selector
What happens when decades of critical infrastructure experience meet today’s rapidly evolving AI landscape? In this episode, host Bob Slevin sits down with Ernie Hayden, award-winning author, former Navy nuclear officer, ethical hacker, and founder of 443 Consulting, for a deep dive into what it truly takes to secure modern, interconnected systems.
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By Selector
See how Selector turns fragmented alerts into actionable insight through intelligent correlation. In this demo, watch how events from across the environment are automatically connected, reducing noise and revealing the true root cause behind incidents. Instead of chasing isolated alerts, teams get a single, clear view of what’s happening and what to do next - faster. Built for network and operations teams who need to cut through noise and resolve issues with confidence.
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By Selector
AIOps is ushering in a new era in which enterprise operations are fully autonomous under the supervision of operations staff. However, this shift requires an evolution of current practices and technologies. In this comprehensive guide, we present a four-stage model for embracing AIOps, going from the lowest level to the highest visionary state.
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Selector uses artificial intelligence, machine learning, and LLM-driven, self-serve analytics to provide instant access to actionable insights and reduce MTTR by up to 90%.
Selector AI is the industry leading AIOps platform designed to provide instant, real-time actionable insights for managing multi-domain network and application infrastructures. By bringing together multiple sources of data into one easy to use platform, IT teams can troubleshoot network issues faster, avoid downtime, reduce MTTR and improve efficiency.
Platform Features:
- Anomaly Detection: Uncover underlying issues sooner with machine learning insight.
- Event Correlation: Identify related issues across multiple data sets to get to root cause.
- Smart Alerting: Cut through alert fatigue with automatic event prioritization.
- Selector Copilot: Leverage your collaboration tools to access analytics.
- Log Analytics: Consolidate and analyze all log data for greater insight.
- Integrations: Integrate easily with your preferred or legacy tools.
The World’s 1st Unified Monitoring and AIOps Platform.