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.

Deploying software to your internal systems or sending releases to external customers is a process that ideally follows strong security requirements in an end-to-end manner. In a perfect world, every software release should come with at least the following guarantees In the world of CI/CD we are mostly interested in binary artifacts and how they were created all the way from the initial release up until they reach production.

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.

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.

The economy and businesses closely rely on network infrastructures functioning efficiently. Minor network bottlenecks and snags can cost companies a good chunk of money and negatively affect their reputation. When there is so much to lose, the natural reaction of organizations is to throw more people and, in turn, more money at the problem.

In the context of IT service management, Service Level Agreements (SLAs) have long been the cornerstone for measuring and ensuring the quality of services provided to customers. However, as technology evolves and incidents become more complex, relying solely on SLAs may not be sufficient. This is where Service Level Objectives (SLOs) come into play, offering a more nuanced approach to Incident Response.