Intent-Directed Dialogue Tuning is the process of adjusting conversations with computer systems so they better understand and respond to the user’s specific goals or intentions. This involves training or tweaking dialogue systems, such as chatbots, to recognise what a user wants and to guide the conversation in that direction. The aim is to make interactions…
Category: Model Training & Tuning
Multi-Model A/B Testing
Multi-Model A/B Testing is a method where multiple machine learning models are tested at the same time to see which one performs best. Each model is shown to a different group of users or data, and their results are compared by measuring key metrics. This approach helps teams choose the most effective model by using…
Prompt Drift Benchmarks
Prompt Drift Benchmarks are tests or standards used to measure how the output of an AI language model changes when the same prompt is used over time or across different versions of the model. These benchmarks help track whether the AI’s responses become less accurate, less consistent, or change in unexpected ways. By using prompt…
Auto-Label via AI Models
Auto-Label via AI Models refers to the process of using artificial intelligence to automatically assign labels or categories to data, such as images, text or audio. This helps save time and reduces manual effort, especially when dealing with large datasets. The AI model learns from examples and applies its understanding to label new, unlabelled data…
Active Drift Mitigation
Active drift mitigation refers to the process of continuously monitoring and correcting changes or errors in a system to keep it performing as intended. This approach involves making real-time adjustments to counteract any unwanted shifts or drifts that may occur over time. It is commonly used in technology, engineering, and scientific settings to maintain accuracy…
Output Stability Tracking
Output stability tracking is the process of monitoring the consistency and reliability of a system’s results over time. It ensures that the output of a device, software, or process remains steady and predictable, even if conditions change. This helps maintain quality, safety, and efficiency in various applications by detecting and correcting any fluctuations or unexpected…
Training Run Explainability
Training run explainability refers to the ability to understand and interpret what happens during the training of a machine learning model. It involves tracking how the model learns, which data points influence its decisions, and why certain outcomes occur. This helps developers and stakeholders trust the process and make informed adjustments. By making the training…
Feedback-Informed Retraining
Feedback-Informed Retraining is a process where systems or models are updated based on feedback about their performance. This feedback can come from users, automated monitoring, or other sources. By retraining using this feedback, the system can improve accuracy, adapt to new requirements, or correct mistakes.
Model Snapshot Comparison
Model snapshot comparison is the process of evaluating and contrasting different saved versions of a machine learning model. These snapshots capture the model’s state at various points during training or after different changes. By comparing them, teams can see how updates, new data, or tweaks affect performance and behaviour, helping to make informed decisions about…
Training Pipeline Optimisation
Training pipeline optimisation is the process of improving the steps involved in preparing, training, and evaluating machine learning models, making the workflow faster, more reliable, and cost-effective. It involves refining data handling, automating repetitive tasks, and removing unnecessary delays to ensure the pipeline runs smoothly. The goal is to achieve better results with less computational…