Category: Model Training & Tuning

ML Optimisation Agent

An ML Optimisation Agent is a computer program or system that automatically improves the performance of machine learning models. It uses feedback and data to adjust the model’s parameters, settings, or strategies, aiming to make predictions more accurate or efficient. These agents can work by trying different approaches and learning from results, so they can…

Forecast Variance Engine

A Forecast Variance Engine is a tool or system that analyses the differences between predicted outcomes and actual results. It helps organisations understand where and why their forecasts, such as sales or budgets, differed from reality. By identifying these discrepancies, teams can adjust their forecasting methods and make better decisions in the future.

AI Training Dashboard

An AI Training Dashboard is an interactive software tool that allows users to monitor, manage, and analyse the process of training artificial intelligence models. It presents information such as progress, performance metrics, errors, and resource usage in an easy-to-understand visual format. This helps users quickly identify issues, compare results, and make informed decisions to improve…

Model Chooser

A Model Chooser is a tool or system that helps users select the most appropriate machine learning or statistical model for a specific task or dataset. It considers factors like data type, problem requirements, and performance goals to suggest suitable models. Model Choosers can be manual guides, automated software, or interactive interfaces that streamline the…

Keyword Boost

Keyword Boost is a strategy used in digital marketing and search engine optimisation to increase the visibility of specific words or phrases within online content. By focusing on these targeted keywords, websites can attract more visitors searching for related topics. This can involve adjusting website text, blog posts, or advertisements to feature the chosen keywords…

Label Errors

Label errors occur when the information assigned to data, such as categories or values, is incorrect or misleading. This often happens during data annotation, where mistakes can result from human error, misunderstanding, or unclear guidelines. Such errors can negatively impact the performance and reliability of machine learning models trained on the data.