Managing observability in cloud-native environments can feel like juggling a thousand things at once. OpenTelemetry makes this easier by becoming a favorite among developers for collecting, processing, and exporting telemetry data without breaking a sweat. Now, let’s talk about the OpenTelemetry Helm Chart. It’s like having a shortcut button for deploying OpenTelemetry in Kubernetes.

The promise of observing your application without writing code (i.e., auto-instrumentation) is not new, and it’s extremely compelling: run a single command in your cluster and suddenly application telemetry starts arriving at your observability backend. What else could you ask for? The OpenTelemetry Operator aims to fulfill such a dream for Kubernetes environments by using a set of well known patterns such as operators and custom resources.

We’re excited to introduce the enhanced Lumigo Kubernetes Operator, now more powerful than ever. With just a quick installation, you gain comprehensive observability—bringing together logs, metrics, and traces in a single platform to provide deeper insights and faster troubleshooting. The improved Lumigo Kubernetes Operator unlocks cluster-wide visibility by collecting key infrastructure metrics and logs—allowing you to monitor, analyze, and optimize with minimal effort.

As the demand for large language models (LLMs) and AI-powered applications continues to grow, businesses are facing challenges in scaling compute capabilities and managing costs. GPUs have become the cornerstone of AI innovation, but their integration requires scalable and efficient solutions tailored to enterprise needs.

Machine learning (ML) has shifted from being a niche research field to a powerhouse behind many technologies we use daily. From personalized recommendations on streaming platforms to chatbots and image recognition, ML’s influence is everywhere. But what exactly is machine learning, and why should you invest time in learning about it? This blog will walk you through ML’s fundamentals, explain what you need to know, and outline a practical step-by-step plan to start your ML journey.

Infrastructure management is critical for companies building and scaling applications. Traditional managed services (MSPs) handle these tasks externally but often come with high costs, slow execution, and limited flexibility. For teams needing control, speed, and efficiency, DevOps automation platforms might offer a much better alternative.

Managing TLS certificates in Kubernetes is no small feat, and the complexity only grows when you’re dealing with multiple clusters. Ensuring secure communication, automating certificate renewals, and integrating with external Certificate Authorities (CAs) are just a few of the challenges Kubernetes administrators, DevOps engineers, and security professionals face.

As businesses evolve in today’s digital landscape, the need for efficient and scalable computing resources has become paramount. In the early days of the Internet, large corporations would build or rent out large data centers to run their applications and serve customers. This was great as they could use dedicated hardware and expand as they pleased.

Technology is advancing rapidly, and with it comes a growing demand for powerful computers, especially in fields such as machine learning (ML), artificial intelligence (AI), and high-performance computing. As these areas develop, the size and complexity of the data they handle also increases. This surge in computing power requirements necessitates new methods for processing large amounts of data efficiently, without sacrificing accuracy or speed.

It’s time to stop proclaiming that “cloud native is the future”. Kubernetes has just celebrated its 10 year anniversary, and 76% of respondents to the latest CNCF Annual Survey reported that they have adopted cloud native technologies, like containers, for much or all of their production development and deployment. Cloud native isn’t the future – it’s here and now. Data-intensive workloads are no exception.