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Machine Learning (ML) is a branch of artificial intelligence that enables systems to automatically learn from data and improve their performance over time without being explicitly programmed. Instead of following fixed rules, ML algorithms identify patterns, make predictions, and adapt…
Feature engineering is the work of turning raw data into something a machine learning model can learn from. Real-world data is often messy, inconsistent, and full of detail that does not help an algorithm. Feature engineering shapes that data into…
Master Data Management (MDM) is the process of creating, maintaining, and governing a single, trusted source of critical business data across an organisation. It ensures that information such as customers, products, employees, and suppliers is accurate, consistent, and up to…
Federated learning is a way of training machine learning models without ever moving the raw data. Instead of sending data to a central system, each participating device or organisation trains the model locally and shares only the model updates. The…
MLOps, short for Machine Learning Operations, is the practice of managing and automating the lifecycle of machine learning models, from development and deployment to monitoring and maintenance. It brings together data science, engineering, and operations to ensure that AI models…
Fine-tuning is the process of taking a pre-trained machine learning or large language model (LLM) and training it further on a smaller, domain-specific dataset. The goal is to adapt the model’s general knowledge to perform better on tasks relevant to…
A Model Card is a transparent documentation report that describes the performance, limitations, and intended use of an artificial intelligence or machine learning model. It helps stakeholders understand how a model was built, what data it was trained on, and…
Customer Journey AI refers to the use of Artificial Intelligence (AI) to map, analyse, and optimise every stage of the customer experience. It combines behavioural data, predictive analytics, and machine learning models to understand how customers interact with a brand,…
Data augmentation is a technique used in machine learning and deep learning to artificially expand the size and diversity of a training dataset. It involves applying transformations, edits, or variations to existing data to create new examples, helping models generalise…
Data governance is the framework of policies, processes, and responsibilities that ensures an organisation’s data is accurate, consistent, secure, and used ethically. It defines how data is collected, stored, managed, and shared to maintain quality, compliance, and accountability across the…