Category: MLOps & Deployment

Model Compression Pipelines

Model compression pipelines are step-by-step processes that reduce the size and complexity of machine learning models while trying to keep their performance close to the original. These pipelines often use techniques such as pruning, quantisation, and knowledge distillation to achieve smaller and faster models. The goal is to make models more suitable for devices with…

Dynamic Model Scheduling

Dynamic model scheduling is a technique where computer models, such as those used in artificial intelligence or simulations, are chosen and run based on changing needs or conditions. Instead of always using the same model or schedule, the system decides which model to use and when, adapting as new information comes in. This approach helps…

Secure Model Inference

Secure model inference refers to techniques and methods used to protect data and machine learning models during the process of making predictions. It ensures that sensitive information in both the input data and the model itself cannot be accessed or leaked by unauthorised parties. This is especially important when working with confidential or private data,…

Cloud Deployment Automation

Cloud deployment automation is the process of using software tools to automatically set up, manage, and update computing resources in the cloud. This removes the need for manual steps, making it faster and less error-prone to launch or update applications and services. By automating these tasks, teams can ensure consistent setups, reduce human mistakes, and…

Data Workflow Optimization

Data workflow optimisation is the process of improving how data moves through different steps in a project or organisation. It involves organising tasks, automating repetitive actions, and removing unnecessary steps to make handling data faster and more reliable. The goal is to reduce errors, save time, and help people make better decisions using accurate data.

Automation Performance Tracking

Automation performance tracking is the process of measuring and analysing how well automated systems or processes are working. It involves collecting data on factors like speed, accuracy, reliability and the number of completed tasks. This information helps organisations understand if their automation tools are delivering the expected benefits and where improvements can be made. By…

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.

TinyML Deployment Strategies

TinyML deployment strategies refer to the methods and best practices used to run machine learning models on very small, resource-constrained devices such as microcontrollers and sensors. These strategies focus on making models small enough to fit limited memory and efficient enough to run on minimal processing power. They also involve optimising power consumption and ensuring…

Edge Inference Optimization

Edge inference optimisation refers to making artificial intelligence models run more efficiently on devices like smartphones, cameras, or sensors, rather than relying on distant servers. This process involves reducing the size of models, speeding up their response times, and lowering power consumption so they can work well on hardware with limited resources. The goal is…