Category: Model Training & Tuning

Decentralized AI Training

Decentralised AI training is a method where multiple computers or devices work together to train an artificial intelligence model, instead of relying on a single central server. Each participant shares the workload by processing data locally and then combining the results. This approach can help protect privacy, reduce costs, and make use of distributed computing…

Multi-Party Model Training

Multi-Party Model Training is a method where several independent organisations or groups work together to train a machine learning model without sharing their raw data. Each party keeps its data private but contributes to the learning process, allowing the final model to benefit from a wider range of information. This approach is especially useful when…

Privacy-Aware Model Training

Privacy-aware model training is the process of building machine learning models while taking special care to protect the privacy of individuals whose data is used. This involves using techniques or methods that prevent the model from exposing sensitive information, either during training or when making predictions. The goal is to ensure that personal details cannot…

Generalization Error Analysis

Generalisation error analysis is the process of measuring how well a machine learning model performs on new, unseen data compared to the data it was trained on. The goal is to understand how accurately the model can make predictions when faced with real-world situations, not just the examples it already knows. By examining the difference…

Domain-Specific Fine-Tuning

Domain-specific fine-tuning is the process of taking a general artificial intelligence model and training it further on data from a particular field or industry. This makes the model more accurate and useful for specialised tasks, such as legal document analysis or medical record summarisation. By focusing on relevant examples, the model learns the specific language,…

Neural Sparsity Optimization

Neural sparsity optimisation is a technique used to make artificial neural networks more efficient by reducing the number of active connections or neurons. This process involves identifying and removing parts of the network that are not essential for accurate predictions, helping to decrease the amount of memory and computing power needed. By making neural networks…

Model Efficiency Metrics

Model efficiency metrics are measurements used to evaluate how effectively a machine learning model uses resources like time, memory, and computational power while making predictions. These metrics help developers understand the trade-off between a model’s accuracy and its resource consumption. By tracking model efficiency, teams can choose solutions that are both fast and practical for…

Robust Training Pipelines

Robust training pipelines are systematic processes for building, testing and deploying machine learning models that are reliable and repeatable. They handle tasks like data collection, cleaning, model training, evaluation and deployment in a way that minimises errors and ensures consistency. By automating steps and including checks for data quality or unexpected issues, robust pipelines help…

Neural Calibration Frameworks

Neural calibration frameworks are systems or methods designed to improve the reliability of predictions made by neural networks. They work by adjusting the confidence levels output by these models so that the stated probabilities match the actual likelihood of an event or classification being correct. This helps ensure that when a neural network says it…