Adaptive learning rates are techniques used in deep learning to automatically adjust how quickly a model learns during training. Instead of keeping the pace of learning constant, these methods change the learning rate based on how the training is progressing. This helps the model learn more efficiently and can prevent problems like getting stuck or…
Category: Model Optimisation Techniques
AI Performance Heatmaps
AI performance heatmaps are visual tools that show how well an artificial intelligence system is working across different inputs or conditions. They use colour gradients to highlight areas where AI models perform strongly or struggle, making it easy to spot patterns or problem areas. These heatmaps help developers and analysts quickly understand and improve AI…
Memory-Constrained Prompt Logic
Memory-Constrained Prompt Logic refers to designing instructions or prompts for AI models when there is a strict limit on how much information can be included at once. This often happens with large language models that have a maximum input size. The aim is to make the most important information fit within these limits so the…
Latency-Aware Prompt Scheduling
Latency-Aware Prompt Scheduling is a method for organising and managing prompts sent to artificial intelligence models based on how quickly they can be processed. It aims to minimise waiting times and improve the overall speed of responses, especially when multiple prompts are handled at once. By considering the expected delay for each prompt, systems can…
Cost-Conscious Inference Models
Cost-conscious inference models are artificial intelligence systems designed to balance accuracy with the cost of making predictions. These costs can include time, computing resources, or even financial expenses related to running complex models. The main goal is to provide reliable results while using as few resources as possible, making them suitable for situations where efficiency…
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…
Query Cost Predictors
Query cost predictors are tools or algorithms that estimate how much computer resources, such as time and memory, a database query will use before it is run. These predictions help database systems choose the most efficient way to process and return the requested information. Accurate query cost prediction can improve performance and reduce waiting times…
Inference Cost Reduction Patterns
Inference cost reduction patterns are strategies used to lower the resources, time, or money needed when running machine learning models to make predictions. These patterns aim to make models faster or cheaper to use, especially in production settings where many predictions are needed. Techniques may include simplifying models, batching requests, using hardware efficiently, or only…
Task-Specific Fine-Tuning
Task-specific fine-tuning is the process of taking a pre-trained artificial intelligence model and further training it using data specific to a particular task or application. This extra training helps the model become better at solving the chosen problem, such as translating languages, detecting spam emails, or analysing medical images. By focusing on relevant examples, the…