Operations | Monitoring | ITSM | DevOps | Cloud

Singapore’s digital economy is growing at a remarkable pace, but with that growth comes a challenge: the nation is on track to need more than a million additional digitally skilled workers by 2026, particularly in cybersecurity, data, and AI. This is not just about filling jobs — it’s about ensuring the country’s long-term digital resilience.

Imagine a future where machines not only perform physical tasks but also learn, adapt, and make intelligent decisions in dynamic environments. This future is rapidly becoming a reality with the advent of smart robots, poised to revolutionize industries from manufacturing to healthcare. In this article, we'll delve into smart robots: what makes these intelligent machines 'smart', how they perform tasks, and how they are reshaping the operational landscape.

In September at.conf25, we announced how Splunk is shaping the future of digital resilience in the age of AI. Agentic AI is rewriting what it takes to build a leading observability practice. As vibe coding gains steam, applications will be built with less human involvement. At the same time, the rise of AI agents demands specialized telemetry to ensure models are performing as intended—aligned to their business purpose and cost.

Splunk is very excited to be sponsoring Kubecon North America once again, kicking off this week in Atlanta, GA. As many know, Splunk is one of the top contributors to the OpenTelemetry project. We’re happy to have sent many of the Splunkers who serve as project maintainers and contributors to lead SIG meetings and engage with the greater community in the OpenTelemetry Observatory, sponsored by Splunk.

Load Balancing is the process of distributing network traffic among multiple server resources. The objective of load balancing is to optimize certain network operations. Ensuring that a workload is spread evenly among the computing resources, this “balanced load” improves application responsiveness and accommodates unexpected traffic spikes — all without compromising application performance. Let’s take a deeper look at this important networking function.

In enterprise boardrooms today, what was once an arms race to adopt more tools and chase every new capability has now crystallized into a single mandate, “Make the platform work harder without spending more.” The industry has reached a saturation point. The buyers who once greenlit expansions now demand efficiency. And the ones who built the stack? They’re rethinking it entirely. It’s no wonder platformization is taking off.

With AI the centerpiece of technology and innovation today, energy efficient computing is quietly becoming one of the most urgent challenges. In this article, we will discuss what makes energy efficient computing relevant for your organization, especially when modern resource-intensive AI workloads play an important role in driving your business operations and services.

Today, organizations looking to build AI products and services using large language models (LLMs), agentic AI, and generative AI often start by investing in artificial intelligence as a service (AIaaS), also known as cloud AI. AIaaS provides a scalable, flexible, and cost-effective way for businesses of all sizes to access advanced AI technologies without the need for extensive in-house expertise or infrastructure.

The RED method is a streamlined approach for monitoring microservices and other request-driven applications, focusing on three critical metrics: Rate, Errors, and Duration. Originating from the principles established by Google's "Four Golden Signals," the RED monitoring framework offers a pragmatic and user-centric perspective on service assurance and service performance.