In modern application development, changes happen constantly: Deployments are pushed, feature flags are toggled, and Kubernetes events reshape infrastructure, to name just a few. While these practices drive innovation and scalability, they also introduce complexity—especially during incidents. Fragmented tools and workflows across teams and organizations make it difficult to pinpoint the root causes of issues, leading to longer resolution times.

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.

In formal terms, OpenTelemetry is an open source framework used for instrumenting, generating, collecting, and exporting telemetry data for applications, services, and infrastructure. It provides vendor-neutral tools, SDKs and APIs for generating, collecting, and exporting telemetry data such as traces, metrics, and logs to any observability backend, including both open source and commercial tools.

Your Kubernetes environment feels perpetually sluggish. Applications that once met performance benchmarks and service-level agreements are now struggling to keep up and operating costs continue to rise. With no clear indicators, you’re left sifting through application logs and dependencies for a root cause.

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.

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.

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.

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.