Cloud-native development is a way of building and running software that is designed to work well in cloud computing environments. It uses tools and practices that make applications easy to deploy, scale, and update across many servers. Cloud-native apps are often made up of small, independent pieces called microservices, which can be managed separately for…
Field-Programmable Gate Arrays, or FPGAs, are special types of computer chips that can be reprogrammed to carry out different tasks even after they have been manufactured. In artificial intelligence, FPGAs are used to speed up tasks such as processing data or running AI models, often more efficiently than traditional processors. Their flexibility allows engineers to…
Secure DevOps pipelines are automated workflows for building, testing, and deploying software, with added security measures at every stage. These pipelines ensure that code is checked for vulnerabilities, dependencies are safe, and sensitive data is protected during development and deployment. The goal is to deliver reliable software quickly, while reducing the risk of security issues.
Kubernetes hardening refers to the process of securing a Kubernetes environment by applying best practices and configuration adjustments. This involves reducing vulnerabilities, limiting access, and protecting workloads from unauthorised use or attacks. Hardening covers areas such as network security, user authentication, resource permissions, and monitoring. By hardening Kubernetes, organisations can better protect their infrastructure, data,…
Memory-constrained inference refers to running artificial intelligence or machine learning models on devices with limited memory, such as smartphones, sensors or embedded systems. These devices cannot store or process large amounts of data at once, so models must be designed or adjusted to fit within their memory limitations. Techniques like model compression, quantisation and streaming…
DevSecOps is a way of working that brings together development, security, and operations teams to create software. It aims to make security a shared responsibility throughout the software development process, rather than something added at the end. By doing this, teams can find and fix security issues earlier and build safer applications faster.
Model drift happens when a machine learning model’s performance worsens over time because the data it sees changes from what it was trained on. This can mean the model makes more mistakes or becomes unreliable. Detecting and fixing model drift is important to keep predictions accurate and useful.
Data pipeline automation is the process of setting up systems that move and transform data from one place to another without manual intervention. It involves connecting data sources, processing the data, and delivering it to its destination automatically. This helps organisations save time, reduce errors, and ensure that data is always up to date.
Model monitoring is the process of regularly checking how a machine learning or statistical model is performing after it has been put into use. It involves tracking key metrics, such as accuracy or error rates, to ensure the model continues to make reliable predictions. If problems are found, such as a drop in performance or…