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…
Category: AI Infrastructure
Continuous Delivery Pipeline
A Continuous Delivery Pipeline is a set of automated steps that take software from development to deployment in a reliable and repeatable way. This process covers everything from testing new code to preparing and releasing updates to users. The goal is to make software changes available quickly and safely, reducing manual work and errors.
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.
AI Model Deployment
AI model deployment is the process of making an artificial intelligence model available for use after it has been trained. This involves setting up the model so that it can receive input data, make predictions, and provide results to users or other software systems. Deployment ensures the model works efficiently and reliably in a real-world…
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…
Analytics Sandbox
An analytics sandbox is a secure, isolated environment where users can analyse data, test models, and explore insights without affecting live systems or production data. It allows data analysts and scientists to experiment with new ideas and approaches in a safe space. The sandbox can be configured with sample or anonymised data to ensure privacy…