The latest News and Information on DevOps, CI/CD, Automation and related technologies.
DevOps has accelerated the delivery of software, but it has also made it more difficult to stay on top of compliance issues and security threats. When applications, environments and infrastructure are constantly changing it becomes increasingly difficult to maintain a handle on compliance and security. For fast-moving teams, real time security monitoring has become essential for quickly identifying risky changes so they can be remediated before they result in security failure.
Modern software delivery teams find themselves under constant pressure to maintain security and compliance without slowing down the speed of development. This usually means that they have to find a way of using automation to ensure robust governance processes that can adapt to evolving cyber threats and new regulatory requirements.
Kubernetes has revolutionized the world of container orchestration, enabling organizations to deploy and manage applications at scale with unprecedented ease and flexibility. Yet, with great power comes great responsibility, and one of the key responsibilities in the Kubernetes ecosystem is resource management. Ensuring that your applications receive the right amount of CPU and memory resources is a fundamental task that impacts the stability and performance of your entire cluster.
As rack densities in data centers increase to support power-hungry applications like Artificial Intelligence and high-performance compute (HPC), data center professionals struggle with the limited cooling capacity and energy efficiency of traditional air cooling systems. In response, a potential solution has emerged in liquid cooling, a paradigm shift from traditional air-based methods that offers a more efficient and targeted approach to thermal management.
Prompt engineering is the practice of crafting input queries or instructions to elicit more accurate and desirable outputs from large language models (LLMs). It is a crucial skill for working with artificial intelligence (AI) applications, helping developers achieve better results from language models. Prompt engineering involves strategically shaping input prompts, exploring the nuances of language, and experimenting with diverse prompts to fine-tune model output and address potential biases.
