Model deployment metrics are measurements used to track the performance and health of a machine learning model after it has been put into use. These metrics help ensure the model is working as intended, making accurate predictions, and serving users efficiently. Common metrics include prediction accuracy, response time, system resource usage, and the rate of…
Category: Data Science
Graph Signal Analysis
Graph signal analysis is a method for studying data that is spread over the nodes of a graph, such as sensors in a network or users in a social network. It combines ideas from signal processing and graph theory to understand how data values change and interact across connected points. This approach helps identify patterns,…
AI-Driven Insights
AI-driven insights are conclusions or patterns identified using artificial intelligence technologies, often from large sets of data. These insights help people and organisations make better decisions by highlighting trends or predicting outcomes that might not be obvious otherwise. The process usually involves algorithms analysing data to find meaningful information quickly and accurately.
Quantum Feature Analysis
Quantum feature analysis is a process that uses quantum computing techniques to examine and interpret the important characteristics, or features, in data. It aims to identify which parts of the data are most useful for making predictions or decisions. This method takes advantage of quantum systems to analyse information in ways that can be faster…
Model Performance Metrics
Model performance metrics are measurements that help us understand how well a machine learning model is working. They show if the model is making correct predictions or mistakes. Different metrics are used depending on the type of problem, such as predicting numbers or categories. These metrics help data scientists compare models and choose the best…
Process Automation Analytics
Process automation analytics involves collecting and analysing data from automated business processes to measure performance, identify bottlenecks, and improve efficiency. By tracking how automated tasks are completed, organisations can spot where things slow down or go wrong. This insight helps businesses make better decisions about how to optimise their processes and get more value from…
Graph-Based Analytics
Graph-based analytics is a way of analysing data by representing it as a network of connected points, called nodes, and relationships, called edges. This approach helps to reveal patterns and connections that might be hard to spot with traditional tables or lists. It is especially useful for understanding complex relationships, such as social networks, supply…
Graph Feature Extraction
Graph feature extraction is the process of identifying and collecting important information from graphs, which are structures made up of nodes and connections. This information can include attributes like the number of connections a node has, the shortest path between nodes, or the overall shape of the graph. These features help computers understand and analyse…
Quantum Data Analysis
Quantum data analysis is the process of using quantum computers and algorithms to examine and interpret complex data. Unlike classical computers, quantum systems can process vast amounts of information at once by leveraging quantum bits, which can exist in multiple states simultaneously. This approach has the potential to solve certain data analysis problems much faster…
Graph-Based Prediction
Graph-based prediction is a method of using data that is organised as networks or graphs to forecast outcomes or relationships. In these graphs, items like people, places, or things are represented as nodes, and the connections between them are called edges. This approach helps uncover patterns or make predictions by analysing how nodes are linked…