Operations | Monitoring | ITSM | DevOps | Cloud

#038 - Kubernetes Supercharging Particle Physics with Ricardo Rocha (CERN)

Ricardo from CERN, who leads the platform infrastructure teams, discusses CERN's significant role in particle physics research with the Large Hadron Collider. The conversation covers how CERN manages the massive amounts of data generated from experiments using a worldwide computing grid. Ricardo shares CERN's journey with adopting Kubernetes for various applications, including critical systems controlling detectors and accelerators. He also touches upon CERN's involvement with the CNCF and the Kubernetes community.

Drift Detection in Kubernetes

When the increasingly popular strategy of configuration as code (CaC) is used to develop infrastructure, it’s known as infrastructure as code (IaC). Today, IaC is quickly becoming entrenched in development processes, especially in conjunction with Terraform and Kubernetes. Yet, although IaC (and CaC) bring immense value, they can also lead to a major problem: configuration drift.

[Webinar] Kubernetes Health Management with Komodor

Modern Kubernetes environments are increasingly growing in scale and complexity. While application performance monitoring and infra-observability tools were once sufficient to maintain reliability, they are ill-equipped to handle the distributed and ever-changing nature of dozens or hundreds of Kubernetes clusters.

#037 - Problem First, Kubernetes Second: Insights from Ahmed Bebars (New York Times | CNCF)

In this episode of Kubernetes for Humans, we speak with Ahmed Bebars, a Principal Engineer at the New York Times and a CNCF Ambassador, who offers a unique perspective on cloud native technologies. Ahmed recounts his professional journey in accounting before transitioning into the technology sector, leading to his current deep involvement with the Kubernetes ecosystem. He shares his initial introduction to Kubernetes almost a decade ago, recognizing its capabilities in container orchestration.

AIOps for Kubernetes (or KAIOps?)

With the growing complexity of cloud-native applications, DevOps teams often face challenges when setting up and maintaining Kubernetes observability. AIOps (artificial intelligence for IT operations) makes the process more manageable using AI and machine learning for monitoring, troubleshooting, and performance optimization. In this article, you’ll learn about the common challenges in Kubernetes observability and how AIOps can provide proactive and effective solutions.

Kubernetes for AI Workloads

Kubernetes has been facilitating container orchestration for around a decade for both stateful and stateless application workloads. With the recent rise of AI and the advent of tools like Kubeflow and Argo Workflows, Kubernetes is also becoming a first-class citizen when it comes to running AI workloads. When you are training a model on K8s, you may be tweaking many parameters and have to test each of them one by one.

#036 - Beyond Kubernetes: A Radical Vision for the Future of Infrastructure with Adam Jacob (Syst...

Adam Jacob, CEO of System Initiative and original author of Chef, discusses the evolution of infrastructure automation and his career-long passion for infrastructure. Jacob reflects on the history and context of Chef, its emergence alongside EC2, and its role in configuration management. He shares insights into the competitive landscape of configuration management tools like Chef, Puppet, and Ansible, and touches upon the transition of Chef to Progress.

The AI Model Showdown - LLaMA 3.3-70B vs. Claude 3.5 Sonnet v2 vs. DeepSeek-R1/V3

Following all the hype and bluster with DeepSeek’s arrival in the AI landscape––and its ability to crash the poster child of AI’s share value overnight (Nvidia), we wanted to conduct a rigorous evaluation at Komodor. We tested DeepSeek’s models head-to-head against industry leaders in solving real-world Kubernetes challenges.