Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes has become the de facto standard for container orchestration, enabling organizations to deploy, scale, and manage containerized applications with ease. However, ensuring the reliability, performance, and security of applications running in a Kubernetes environment requires robust monitoring and observability solutions.

In the ever-changing field of artificial intelligence, OpenAI is consistently seen as a leader in innovation. Its AI models, starting with GPT-3 and now with GPT-4, are already used extensively in software development and content creation, and they’re expected to usher in entire sets of new systems in the future.

KubeCon + CloudNativeCon North America 2023 is just around the corner, and the OSS and cloud native community is eagerly anticipating the event, which will take place November 6 - 9 in Chicago.

Use Levitate's powerful domain events to track success of canary rollouts via ArgoCD.

For many developers and engineers, Kubernetes is the de facto choice for container orchestration. That’s primarily because of its efficiency in handling and scaling container workloads. However, the complexity of managing nodes in a Kubernetes cluster can cause recurring headaches for even the most experienced and skilled IT teams. This is where `kubectl cordon` comes into play.

What is OpenTelemetry? Simply put, OpenTelemetry is an open source observability framework. It offers vendor-agnostic or vendor-neutral APIs, software development kits (SDKs) and other tools for collecting telemetry data from cloud-native applications and their supporting infrastructure to understand their performance and health. Managing performance in today’s complex, distributed environment is extremely difficult.

What is AIOps? Simply put, AIOps uses big data, analytics and machine learning to automate and improve IT operations (ITOps). AI is particularly important in ITOps functions such as anomaly detection and event correlation, as it has the ability to analyze large volumes of network and machine data to find patterns, identify the cause of existing problems and find ways to forecast and prevent future issues.

Broadband subscriber behavior analysis is the process of collecting and analyzing data on how broadband subscribers use the internet. This data can be used to gain insights into subscriber needs and preferences, as well as to identify potential problems with the broadband service.