Operations | Monitoring | ITSM | DevOps | Cloud

Top tips is a weekly column where we highlight what’s trending in the tech world today and list out ways to explore these trends. This week we’re looking at five ways ways you can build upon the basics and start incorporating AI in your everyday. AI technology is now utilized in some form by almost 77% of devices. Nearly every industry has incorporated, or is trying to incorporate, AI in some way or another.

Let’s take a nostalgic trip down memory lane of traditional IT helpdesks. It’s reminiscent of waiting in long lines only to be told you’re in the wrong line. Or being serenaded by never-ending loops of elevator music? But, in a world where businesses are now laser-focused on customer success, these methods are antiquated and impractical. Don’t believe me? Ask any customer success manager.

OpenTelemetry is the future of Observability, APM, Monitoring, whatever you want to call ‘the process of knowing what our software is doing.’ It’s becoming common knowledge that your time is better spent gaining experience with an open, standardized system for telemetry than closed-source or otherwise proprietary standard. This truth is so universally acknowledged that all the big players in the market have made announcements of how they’re embracing OpenTelemetry.

AI is booming. The AI market is projected to grow 37.3% annually from 2023 to 2030. With so many organizations adopting or considering AI applications, data centers need to be ready to support the new demand. However, without the right tools and data, it is difficult to understand if your existing facilities have the capacity to support systems like the “gold standard for AI infrastructure,” the NVIDIA DGX H100.

In this tutorial, we will learn about configuring Filebeat to run as a DaemonSet in our Kubernetes cluster in order to ship logs to the Elasticsearch backend. We are using Filebeat instead of FluentD or FluentBit because it is an extremely lightweight utility and has a first-class support for Kubernetes. It is best for production-level setups. This blog post is the second in a two-part series. The first post runs through the deployment architecture for the nodes and deploying Kibana and ES-HQ.

This is the first post of a 2 part series where we will set up production-grade Kubernetes logging for applications deployed in the cluster and the cluster itself. We will be using Elasticsearch as the logging backend for this. The Elasticsearch setup will be extremely scalable and fault-tolerant. ‍

A Key Management Service (KMS) is used to create and manage cryptographic keys and control their usage across various platforms and applications. If you are an AWS user, you must have heard of or used its managed Key Management Service called AWS KMS. This service allows users to manage keys across AWS services and hosted applications in a secure way.