Category: AI Infrastructure

Quantum Noise Calibration

Quantum noise calibration is the process of measuring and adjusting for random fluctuations that affect quantum systems, such as quantum computers or sensors. These fluctuations, or noise, can interfere with the accuracy of quantum operations and measurements. By calibrating for quantum noise, engineers and scientists can improve the reliability and precision of quantum devices.

Model Deployment Frameworks

Model deployment frameworks are software tools or platforms that help move machine learning models from development into live environments where people or systems can use them. They automate tasks like packaging, serving, monitoring, and updating models, making the process more reliable and scalable. These frameworks simplify the transition from building a model to making it…

Quantum Error Calibration

Quantum error calibration is the process of identifying, measuring, and adjusting for errors that can occur in quantum computers. Because quantum bits, or qubits, are extremely sensitive to their environment, they can easily be disturbed and give incorrect results. Calibration helps to keep the system running accurately by fine-tuning the hardware and software so that…

Model Inference Frameworks

Model inference frameworks are software tools or libraries that help run trained machine learning models to make predictions on new data. They handle tasks like loading the model, preparing input data, running the calculations, and returning results. These frameworks are designed to be efficient and work across different hardware, such as CPUs, GPUs, or mobile…

Cloud Resource Optimization

Cloud resource optimisation is the process of managing and adjusting the use of cloud services to achieve the best performance at the lowest possible cost. It involves analysing how much computing power, storage, and network resources are being used and making changes to avoid waste or unnecessary expenses. This can include resizing virtual machines, shutting…

Quantum Circuit Efficiency

Quantum circuit efficiency refers to how effectively a quantum circuit uses resources such as the number of quantum gates, the depth of the circuit, and the number of qubits involved. Efficient circuits achieve their intended purpose using as few steps, components, and time as possible. Improving efficiency is vital because quantum computers are currently limited…

Cloud-Native Monitoring

Cloud-native monitoring is the process of observing and tracking the performance, health, and reliability of applications built to run on cloud platforms. It uses specialised tools to collect data from distributed systems, containers, and microservices that are common in cloud environments. This monitoring helps teams quickly detect issues, optimise resources, and ensure that services are…

Model Deployment Metrics

Model deployment metrics are measurements used to track the performance and health of a machine learning model after it has been put into use. These metrics help ensure the model is working as intended, making accurate predictions, and serving users efficiently. Common metrics include prediction accuracy, response time, system resource usage, and the rate of…