Inference optimisation refers to making machine learning models run faster and more efficiently when they are used to make predictions. It involves adjusting the way a model processes data so that it can deliver results quickly, often with less computing power. This is important for applications where speed and resource use matter, such as mobile…
Model serving optimisation is the process of making machine learning models respond faster and use fewer resources when they are used in real applications. It involves improving how models are loaded, run, and scaled to handle many requests efficiently. The goal is to deliver accurate predictions quickly while keeping costs low and ensuring reliability.
Data pipeline optimisation is the process of improving how data moves from one place to another, making it faster, more reliable, and more cost-effective. It involves looking at each step of the pipeline, such as collecting, cleaning, transforming, and storing data, to find ways to reduce delays and resource use. By refining these steps, organisations…
Dashboard optimisation is the process of improving dashboards so that they display information clearly and efficiently. It involves arranging data, charts, and metrics in a way that makes them easy to understand at a glance. The goal is to help users make better decisions by presenting the most important information in a logical and visually…
Performance metrics design is the process of deciding which measurements best reflect how well a system, process, or team is achieving its goals. It involves choosing clear, relevant indicators that can be tracked and analysed over time. Good metric design helps organisations understand progress, identify areas for improvement, and make informed decisions.
Test coverage metrics are measurements that show how much of your software’s code is tested by automated tests. They help teams understand if important parts of the code are being checked for errors. By looking at these metrics, teams can find parts of the code that might need more tests to reduce the risk of…
Exploration-Exploitation Strategies are approaches used to balance trying new options with using known, rewarding ones. The aim is to find the best possible outcome by sometimes exploring unfamiliar choices and sometimes sticking with what already works. These strategies are often used in decision-making systems, such as recommendation engines or reinforcement learning, to improve long-term results.
Cloud workload optimisation is the process of making sure that applications and tasks running in a cloud environment use resources efficiently. This includes managing how much computing power, storage, and network capacity each workload needs, so that costs are kept low and performance stays high. By monitoring and adjusting resources as needed, organisations avoid waste…
Statistical model validation is the process of checking whether a statistical model accurately represents the data it is intended to explain or predict. It involves assessing how well the model performs on new, unseen data, not just the data used to build it. Validation helps ensure that the model’s results are trustworthy and not just…
Neural network regularisation refers to a group of techniques used to prevent a neural network from overfitting to its training data. Overfitting happens when a model learns the training data too well, including its noise and outliers, which can cause it to perform poorly on new, unseen data. Regularisation methods help the model generalise better…