Category: Model Optimisation Techniques

Throughput Analysis

Post author By EfficiencyAI
Post date 5 June 2025
Categories In AI Infrastructure, Data Engineering, Model Optimisation Techniques

Throughput analysis is the process of measuring how much work or data can pass through a system or process in a specific amount of time. It helps identify the maximum capacity and efficiency of systems, such as computer networks, manufacturing lines, or software applications. By understanding throughput, organisations can spot bottlenecks and make improvements to…

Output Delay

Post author By EfficiencyAI
Post date 5 June 2025
Categories In AI Infrastructure, MLOps & Deployment, Model Optimisation Techniques

Output delay is the time it takes for a system or device to produce a result after receiving an input or command. It measures the lag between an action and the system’s response that is visible or usable. This delay can occur in computers, electronics, networks, or any process where outputs rely on earlier actions…

Batch Pacing

Post author By EfficiencyAI
Post date 5 June 2025
Categories In AI Infrastructure, Model Optimisation Techniques, Model Training & Tuning

Batch pacing is a method used to control the speed and timing at which groups of tasks, jobs or items are processed in a system. It helps ensure that resources are used efficiently and prevents bottlenecks by spacing out the workload over time. Batch pacing is often used in manufacturing, software processing, and online advertising…

Model Memory

Post author By EfficiencyAI
Post date 5 June 2025
Categories In Artificial Intelligence, Model Optimisation Techniques, Model Training & Tuning

Model memory refers to the way an artificial intelligence model stores and uses information from previous interactions or data. It helps the model remember important details, context, or patterns so it can make better predictions or provide more relevant responses. Model memory can be short-term, like recalling the last few conversation turns, or long-term, like…

Output Depth

Post author By EfficiencyAI
Post date 5 June 2025
Categories In Deep Learning, Model Optimisation Techniques, Model Training & Tuning

Output depth refers to the number of bits used to represent each individual value in digital output, such as in images, audio, or video. It determines how many distinct values or shades can be displayed or recorded. For example, higher output depth in an image means more subtle colour differences can be shown, resulting in…

Error Rewriting

Post author By EfficiencyAI
Post date 5 June 2025
Categories In Artificial Intelligence, MLOps & Deployment, Model Optimisation Techniques

Error rewriting is the process of changing or transforming error messages produced by a computer program or system. This is usually done to make errors easier to understand, more helpful, or more secure by hiding technical details. Developers use error rewriting to ensure users or other systems receive clear and actionable information when something goes…

Output Batching

Post author By EfficiencyAI
Post date 5 June 2025
Categories In AI Infrastructure, Model Optimisation Techniques, Model Training & Tuning

Output batching is a technique where multiple pieces of output data are grouped together and sent or processed at the same time, instead of handling each item individually. This can make systems more efficient by reducing the number of separate actions needed. It is commonly used in computing, machine learning, and data processing to improve…

Epoch Reduction

Post author By EfficiencyAI
Post date 5 June 2025
Categories In Deep Learning, Model Optimisation Techniques, Model Training & Tuning

Epoch reduction is a technique used in machine learning and artificial intelligence where the number of times a model passes through the entire training dataset, called epochs, is decreased. This approach is often used to speed up the training process or to prevent the model from overfitting, which can happen if the model learns the…

Loss Decay

Post author By EfficiencyAI
Post date 5 June 2025
Categories In Deep Learning, Model Optimisation Techniques, Model Training & Tuning

Loss decay is a technique used in machine learning where the influence of the loss function is gradually reduced during training. This helps the model make larger adjustments in the beginning and smaller, more precise tweaks as it improves. The approach can help prevent overfitting and guide the training process to a more stable final…

Feature Ranking

Post author By EfficiencyAI
Post date 5 June 2025
Categories In Data Science, Model Optimisation Techniques, Model Training & Tuning

Feature ranking is the process of ordering the input variables of a dataset by their importance or relevance to a specific outcome or prediction. It helps identify which features have the most influence on the results of a model, allowing data scientists to focus on the most significant factors. This technique can make models simpler,…