Performance optimisation is the process of making a system, application, or device work faster and use fewer resources. It often involves finding and removing areas that slow things down or waste energy. The goal is to deliver results more quickly and efficiently, which can lead to better user experiences and lower operating costs.
Cloud workload optimisation is the process of adjusting and managing computing resources in the cloud to ensure applications run efficiently and cost-effectively. It involves analysing how resources such as storage, computing power, and networking are used, then making changes to reduce waste and improve performance. The goal is to match the resources provided with what…
Feature selection strategy is the process of choosing which variables or inputs to use in a machine learning model. The goal is to keep only the most important features that help the model make accurate predictions. This helps reduce noise, improve performance, and make the model easier to understand.
A model scalability strategy is a plan for how to grow or adapt a machine learning model to handle larger amounts of data, more users, or increased complexity. This involves choosing methods and tools that let the model work efficiently as demands increase. Without a good scalability strategy, a model might become too slow, inaccurate,…
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…