Model inference frameworks are software tools or libraries that help run machine learning models to make predictions or decisions using new data. They focus on efficiently using trained models, often optimising for speed, memory usage, and hardware compatibility. These frameworks support deploying models on various devices, such as servers, mobile phones, or embedded systems.
Category: AI Infrastructure
Quantum Noise Optimization
Quantum noise optimisation refers to methods and techniques used to reduce unwanted disturbances, or noise, in quantum systems. Quantum noise can disrupt the behaviour of quantum computers and sensors, making results less accurate. Optimising against this noise is crucial for improving the reliability and efficiency of quantum technologies.
Quantum Circuit Analysis
Quantum circuit analysis is the process of studying and understanding how a quantum circuit works. Quantum circuits use quantum bits, or qubits, and quantum gates to perform calculations that classical computers cannot easily do. Analysing a quantum circuit involves tracking how information changes as it passes through different gates and understanding the final result produced…
Cloud Cost Monitoring
Cloud cost monitoring is the process of tracking and analysing expenses related to using cloud services. It helps organisations understand how much they are spending on things like storage, computing power, and data transfer. By monitoring these costs, businesses can identify areas where they might be overspending and make informed decisions to optimise their cloud…
Quantum Data Optimization
Quantum data optimisation is the process of organising and preparing data so it can be used efficiently by quantum computers. This often means reducing the amount of data or arranging it in a way that matches how quantum algorithms work. The goal is to make sure the quantum computer can use its resources effectively and…
Data Pipeline Frameworks
Data pipeline frameworks are software tools or platforms used to move, process, and manage data from one place to another. They help automate the steps required to collect data, clean it, transform it, and store it in a format suitable for analysis or further use. These frameworks make it easier and more reliable to handle…
Cloud-Native Frameworks
Cloud-native frameworks are sets of tools and libraries designed to help developers build and run applications that fully use the benefits of cloud computing. These frameworks support features like automatic scaling, resilience, and easy updates, making it simpler to manage complex software. They often encourage breaking software into small, manageable parts that can be deployed…
Model Inference Systems
Model inference systems are software tools or platforms that use trained machine learning models to make predictions or decisions based on new data. They take a model that has already learned from historical information and apply it to real-world inputs, producing useful outputs such as answers, classifications, or recommendations. These systems are often used in…
Quantum State Calibration
Quantum state calibration is the process of adjusting and fine-tuning a quantum system so that its quantum states behave as expected. This involves measuring and correcting for errors or inaccuracies in the way quantum bits, or qubits, are prepared, manipulated, and read out. Accurate calibration is essential for reliable quantum computations, as even small errors…
Quantum Circuit Optimization
Quantum circuit optimisation is the process of improving the structure and efficiency of quantum circuits, which are the sequences of operations run on quantum computers. By reducing the number of gates or simplifying the arrangement, these optimisations help circuits run faster and with fewer errors. This is especially important because current quantum hardware has limited…