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…
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 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…
Machine Learning Operations, often called MLOps, is a set of practices that helps organisations manage machine learning models through their entire lifecycle. This includes building, testing, deploying, monitoring, and updating models so that they work reliably in real-world environments. MLOps brings together data scientists, engineers, and IT professionals to ensure that machine learning projects run…
Hybrid data architecture is a way of organising and managing data that combines both traditional on-premises systems and cloud-based solutions. This approach allows organisations to store some data locally for control or security reasons, while using the cloud for scalability and flexibility. It helps businesses use the strengths of both environments, making it easier to…
Data synchronisation is the process of ensuring that information stored in different places remains consistent and up to date. When data changes in one location, synchronisation makes sure the same change is reflected everywhere else it is stored. This is important for preventing mistakes and keeping information accurate across devices or systems.
Data orchestration is the process of managing and coordinating the movement and transformation of data between different systems and tools. It ensures that data flows in the right order, at the right time, and reaches the correct destinations. This helps organisations automate and streamline complex data workflows, making it easier to use data effectively.
Event stream processing is a way of handling data as it arrives, rather than waiting for all the data to be collected first. It allows systems to react to events, such as user actions or sensor readings, in real time. This approach helps organisations quickly analyse, filter, and respond to information as it is generated.
Stream processing strategy is a method for handling data that arrives continuously, like sensor readings or online transactions. Instead of storing all the data first and analysing it later, stream processing analyses each piece of data as it comes in. This allows decisions and actions to be made almost instantly, which is important for systems…