In a previous blog post, which was a glossary of terms related to artificial intelligence, I included this brief definition of "feature extraction": Let’s go a bit deeper on that. In the ever-expanding landscape of machine learning, feature extraction stands out as a crucial technique for enhancing the performance of models and uncovering valuable insights from complex datasets.

At ilert, we are dedicated to providing an effortless, seamless connection between our incident management platform and other popular tools that empower teams to excel in operations. We're excited to introduce two new integrations from the Halo suite: HaloITSM and HaloPSA.

The concept of observability has become a cornerstone for ensuring system reliability and efficiency in modern software engineering and operations. Observability, beyond its traditional scope of logging, monitoring, and tracing, can be intricately defined through the lens of incident response efficiency—specifically by examining the time it takes for teams to grasp the full context and background of a technical incident.

We get it. FinOps can be challenging to understand, implement, and optimize, whether you’ve done it before or are just getting started. Everybody needs help from time to time. If this describes you right now, then check out the following FinOps services companies to get started. But first, a quick background.

RapidSpike recently launched the first of six new features designed to further boost the security of ecommerce websites, in readiness for PCI DSS 4.0. We recently featured in Prolific North. If you missed the write up, you can catch up in full, here… In response to rising ecommerce threats, the Payment Card Industry Data Security Standards will impose 63 new requirements on brands processing, storing or transmitting credit card information, with version 4.0 coming into effect on March 31, 2025.

Telecommunications companies (telcos) are well on their way to transforming their infrastructure from the legacy, unadaptable, complex network of dedicated hardware from yesteryears to agile, modular and scalable software-defined systems running on common off-the-shelf (COTS) servers. Within this space, the current trend, driven by 5G deployments, is to complement tried and tested network function virtualisation (NFV) infrastructure with cloud-native network functions (CNFs).

In our previous post, we discussed how to generate Images using Stable Diffusion on AWS. In this post, we will guide you through running LLMs for text generation in your own environment with a GPU-based instance in simple steps, empowering you to create your own solutions. Text generation, a trending focus in generative AI, facilitates a broad spectrum of language tasks beyond simple question answering.