Category: MLOps & Deployment

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…

Experimentation Platform

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.

Model Monitoring Framework

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…

Model Versioning Strategy

A model versioning strategy is a method for tracking and managing different versions of machine learning models as they are developed, tested, and deployed. It helps teams keep organised records of changes, improvements, or fixes made to each model version. This approach prevents confusion, supports collaboration, and allows teams to revert to previous versions if…

Model Lifecycle Management

Model lifecycle management is the process of overseeing the development, deployment, monitoring, and retirement of machine learning models. It ensures that models are built, tested, deployed, and maintained in a structured way. This approach helps organisations keep their models accurate, reliable, and up-to-date as data or requirements change.

Machine Learning Operations

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…