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.

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.

Artificial intelligence (AI) and machine learning operations (MLOps) have become crucial across a wide swath of industries, with the two technologies working in tandem to provide value. AI enables data-driven insights and automation, while MLOps ensures efficient management of AI models throughout their lifecycle. With AI’s growing complexity and scale, organizations need robust infrastructure to manage intensive computational tasks, giving rise to platforms like Kubernetes.

As someone deeply embedded in the cloud-native space, I’ve witnessed how the rapid evolution of cloud computing has unlocked remarkable opportunities—but also introduced significant challenges. Recently, I revisited some research we conducted back in 2022, which revealed a critical trend: despite the hundreds of services offered by the major cloud providers (the Big Three hyperscalers), most businesses use fewer than 10 services from their cloud provider.