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…
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 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…
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…
An experimentation platform is a software system that helps organisations test ideas, features, or changes by running experiments and analysing their impact. It allows teams to compare different versions of a product or service, usually through methods like A/B testing. The platform collects data, manages experiment groups, and provides results to guide decision-making.
An A/B testing framework is a set of tools and processes that helps teams compare two or more versions of something, such as a webpage or app feature, to see which one performs better. It handles splitting users into groups, showing each group a different version, and collecting data on how users interact with each…
A model retraining strategy is a planned approach for updating a machine learning model with new data over time. As more information becomes available or as patterns change, retraining helps keep the model accurate and relevant. The strategy outlines how often to retrain, what data to use, and how to evaluate the improved model before…
A model monitoring framework is a set of tools and processes used to track the performance and health of machine learning models after they have been deployed. It helps detect issues such as data drift, model errors, and unexpected changes in predictions, ensuring the model continues to function as expected over time. Regular monitoring allows…