Chaos Engineering is a method of testing computer systems by intentionally introducing problems or failures to see how well the system can handle unexpected issues. The goal is to find weaknesses before real problems cause outages or data loss. By simulating faults in a controlled way, teams can improve their systems’ reliability and resilience.
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…
DevOps automation refers to using technology to automatically manage and execute tasks within software development and IT operations. This includes activities like building, testing, deploying, and monitoring applications without manual intervention. By automating these repetitive processes, teams can deliver software faster, reduce errors, and improve consistency across systems.
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 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…
API Lifecycle Management is the process of planning, designing, developing, testing, deploying, maintaining, and retiring application programming interfaces (APIs). It helps ensure that APIs are reliable, secure, and meet the needs of both developers and end users. Good API lifecycle management streamlines updates, tracks usage, and simplifies support over time.
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 drift detection is the process of identifying when a machine learning model’s performance declines because the data it sees has changed over time. This can happen if the real-world conditions or patterns that the model was trained on are no longer the same. Detecting model drift helps ensure that predictions remain accurate and trustworthy…
An AI monitoring framework is a set of tools, processes, and guidelines designed to track and assess the behaviour and performance of artificial intelligence systems. It helps organisations ensure their AI models work as intended, remain accurate over time, and comply with relevant standards or laws. These frameworks often include automated alerts, regular reporting, and…
Inference optimisation refers to making machine learning models run faster and more efficiently when they are used to make predictions. It involves adjusting the way a model processes data so that it can deliver results quickly, often with less computing power. This is important for applications where speed and resource use matter, such as mobile…