Decentralised AI frameworks are systems that allow artificial intelligence models to be trained, managed, or run across multiple computers or devices, rather than relying on a single central server. This approach helps improve privacy, share computational load, and reduce the risk of a single point of failure. By spreading tasks across many participants, decentralised AI…
Category: AI Infrastructure
Federated Learning Scalability
Federated learning scalability refers to how well a federated learning system can handle increasing numbers of participants or devices without a loss in performance or efficiency. As more devices join, the system must manage communication, computation, and data privacy across all participants. Effective scalability ensures that the learning process remains fast, accurate, and secure, even…
Model Optimization Frameworks
Model optimisation frameworks are software tools or libraries that help improve the efficiency, speed, and resource use of machine learning models. They provide methods to simplify or compress models, making them faster to run and easier to deploy, especially on devices with limited computing power. These frameworks often automate tasks like reducing model size, converting…
Robust Inference Pipelines
Robust inference pipelines are organised systems that reliably process data and make predictions using machine learning models. These pipelines include steps for handling input data, running models, and checking results to reduce errors. They are designed to work smoothly even when data is messy or unexpected problems happen, helping ensure consistent and accurate outcomes.
Neural Module Integration
Neural module integration is the process of combining different specialised neural network components, called modules, to work together as a unified system. Each module is trained to perform a specific task, such as recognising objects, understanding language, or making decisions. By integrating these modules, a system can handle more complex problems than any single module…
Real-Time Data Pipelines
Real-time data pipelines are systems that collect, process, and move data instantly as it is generated, rather than waiting for scheduled batches. This approach allows organisations to respond to new information immediately, making it useful for time-sensitive applications. Real-time pipelines often use specialised tools to handle large volumes of data quickly and reliably.
Cloud-Native Observability
Cloud-native observability is the practice of monitoring, measuring and understanding the health and performance of applications that run in cloud environments. It uses tools and techniques designed specifically for modern, distributed systems like microservices and containers. This approach helps teams quickly detect issues, analyse trends and maintain reliable services even as systems scale and change.
AI Accelerator Design
AI accelerator design involves creating specialised hardware that speeds up artificial intelligence tasks like machine learning and deep learning. These devices are built to process large amounts of data and complex calculations more efficiently than general-purpose computers. By focusing on the specific needs of AI algorithms, these accelerators help run AI applications faster and use…
Neuromorphic Processing Units
Neuromorphic Processing Units are specialised computer chips designed to mimic the way the human brain processes information. They use networks of artificial neurons and synapses to handle tasks more efficiently than traditional processors, especially for pattern recognition and learning. These chips consume less power and can process sensory data quickly, making them useful for applications…
Quantum Neural Networks
Quantum neural networks are a type of artificial intelligence model that combines ideas from quantum computing and traditional neural networks. They use quantum bits, or qubits, which can process information in more complex ways than normal computer bits. This allows quantum neural networks to potentially solve certain problems much faster or more efficiently than classical…