Operations | Monitoring | ITSM | DevOps | Cloud

Data-centric AI is the new frontier in AI, where the models themselves now remain stationary while tools, techniques and engineering practices improve data quality. As Andrew Ng puts it, “Data-centric AI is the discipline of systematically engineering data to build an AI system.”

One of the major trends in software development in the last decade has been “shifting left” responsibilities that have traditionally been under operation’s domain to earlier in the software development life cycle (SDLC). It first came in the form of DevOps where a lot of the software engineering best practices were introduced to the deploy, operate, monitor phases. Such examples include continuous integration and continuous deployment (CI/CD) and Infrastructure as Code (IaC).

Many companies are using cloud technologies to become more agile, scalable, and cost-effective during their digital transformation. However, this change brings new challenges in maintaining the security and performance of applications and infrastructure in the cloud. Security and observability go hand-in-hand.

At LogicMonitor, we are deeply committed to a mission that goes beyond the conventional: revolutionizing IT monitoring through hybrid observability powered by AI. This ambition is not merely a slogan but the cornerstone of our entire approach. Our LM Envision platform was purposely designed to bring together diverse IT environments under one seamless, integrated experience. Enterprises have complex IT ecosystems.

The utilization of cloud-based technologies developed to optimize and streamline business operations is far from a novel idea. In fact, research suggests at least 90% of modern organizations currently use cloud platforms and related technologies to oversee essential processes.

We at Komodor are excited to announce our groundbreaking integration with Cisco Full-Stack Observability (FSO). This collaboration marks a significant milestone in Kubernetes Continuous Reliability, bringing together the best of both worlds to redefine Kubernetes management.

In today’s economic and regulatory environment, data sovereignty is increasingly top of mind for observability teams. The rules and regulations surrounding telemetry data can often be challenging to interpret, leaving many teams in the dark about what kind of data they can capture, how long it can be stored, and where it has to reside. In the past, addressing these issues at scale was a costly endeavor.