Operations | Monitoring | ITSM | DevOps | Cloud

In the world of datacenter management, it’s not uncommon to encounter a complex maze of networks and servers. Over time, these environments can become fragmented and difficult to navigate, making it challenging to maintain a comprehensive inventory of your IT assets.

After decades as a software engineer, I’ve seen firsthand how complexity can grow exponentially with scale. Thankfully, over that time, quite a few tools have come along to help manage some of that complexity. One great example is the Internal Developer Portals (IDPs), which were built to connect an engineering organization’s architecture, processes, documentation, and definitions and alignment to standards of health.

Kubernetes annotations are key-value pairs attached to Kubernetes objects, providing a flexible way to extend the functionality of your Kubernetes resources without altering their internal specifications. These annotations serve as a tool to store additional metadata to tailor behavior, orchestrate tools, and interact seamlessly with third-party utilities that complement your Kubernetes environment.

In the last years, Kubernetes has grown tremendously and is considered by most companies to be the best platform to run applications today. In simple words, Kubernetes is an open-source container orchestration platform that allows you to run and manage containerized applications at scale. In this article, I will explain how you can run Kubernetes on AWS in 3 different ways. But before getting down the road, let me explain why it does make sense to run Kubernetes on AWS.

In the ever-evolving landscape of cloud computing, Azure stands out as a powerhouse for businesses seeking scalable, reliable, and efficient solutions. One of the fundamental aspects of Azure’s appeal is its vast array of resources catering to diverse needs. Azure offers a rich ecosystem of resources, ranging from virtual machines (VMs) and storage accounts to databases, networking components, and beyond.

Just a few years back, developing AI/ML (Machine Learning) models was a secluded endeavor, primarily undertaken by small teams of developers and data scientists away from public scrutiny. However, with the surge in GenAI/LLMs, open-source models, and ML development tools, there’s been a significant democratization of model creation, with more developers and organizations engaging in ML model development than ever before.