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A Large Language Model (LLM) is a type of artificial intelligence designed to understand, generate, and interact with human language. Trained on massive amounts of text data, LLMs use deep learning techniques to identify linguistic patterns, predict text sequences, and…
Latency refers to the time delay between an input and its corresponding output in a system. In artificial intelligence and machine learning, latency measures how long it takes for a model to process data and return a prediction, response, or…
Latent space refers to the hidden, multidimensional representation of data that a machine learning model learns during training. It’s where complex, high-dimensional information such as images, text, or sounds is compressed into numerical vectors that capture underlying patterns, relationships, and…
Explainability in AI refers to the ability to understand and interpret how an artificial intelligence or machine learning model arrives at its predictions or decisions. It answers the question: “Why did the AI make that choice?” As AI becomes more…
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…