Category: AI Infrastructure

Observability Framework

An observability framework is a set of tools and practices that help teams monitor, understand, and troubleshoot their software systems. It collects data such as logs, metrics, and traces, presenting insights into how different parts of the system are behaving. This framework helps teams detect issues quickly, find their causes, and ensure systems run smoothly.

Site Reliability Engineering

Site Reliability Engineering (SRE) is a discipline that applies software engineering principles to ensure that computer systems are reliable, scalable, and efficient. SRE teams work to keep services up and running smoothly, prevent outages, and quickly resolve any issues that arise. They use automation and monitoring to manage complex systems and maintain a balance between…

Configuration Management

Configuration management is the process of systematically handling changes to a system, ensuring that the system remains consistent and reliable as it evolves. It involves tracking and controlling every component, such as software, hardware, and documentation, so that changes are made in a controlled and predictable way. This helps teams avoid confusion, prevent errors, and…

Infrastructure as Code

Infrastructure as Code is a method for managing and provisioning computer data centres and cloud resources using machine-readable files instead of manual processes. This approach allows teams to automate the setup, configuration, and maintenance of servers, networks, and other infrastructure. By treating infrastructure like software, changes can be tracked, tested, and repeated reliably.

Cloud Workload Optimization

Cloud workload optimisation is the process of adjusting and managing computing resources in the cloud to ensure applications run efficiently and cost-effectively. It involves analysing how resources such as storage, computing power, and networking are used, then making changes to reduce waste and improve performance. The goal is to match the resources provided with what…

Feature Store Implementation

Feature store implementation refers to the process of building or setting up a system where machine learning features are stored, managed, and shared. This system helps data scientists and engineers organise, reuse, and serve data features consistently for training and deploying models. It ensures that features are up-to-date, reliable, and easily accessible across different projects…

Model Serving Optimization

Model serving optimisation is the process of making machine learning models respond faster and use fewer resources when they are used in real applications. It involves improving how models are loaded, run, and scaled to handle many requests efficiently. The goal is to deliver accurate predictions quickly while keeping costs low and ensuring reliability.

Data Science Workbench

A Data Science Workbench is a software platform that provides tools and environments for data scientists to analyse data, build models, and collaborate on projects. It usually includes features for writing code, visualising data, managing datasets, and sharing results with others. These platforms help streamline the workflow by combining different data science tools in one…