Model Performance Automation refers to the use of software tools and processes that automatically monitor, evaluate, and improve the effectiveness of machine learning models. Instead of manually checking if a model is still making accurate predictions, automation tools can track model accuracy, detect when performance drops, and even trigger retraining without human intervention. This approach…
Category: MLOps & Deployment
Data Pipeline Metrics
Data pipeline metrics are measurements that help track and evaluate the performance, reliability and quality of a data pipeline. These metrics can include how long data takes to move through the pipeline, how many records are processed, how often errors occur, and whether data arrives on time. By monitoring these values, teams can quickly spot…
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 manage the process of loading models, running them efficiently on different hardware, and handling inputs and outputs. These frameworks are designed to optimise speed and resource use so that models can be deployed…
Data Pipeline Frameworks
Data pipeline frameworks are software tools or platforms that help manage the movement and transformation of data from one place to another. They automate tasks such as collecting, cleaning, processing, and storing data, making it easier for organisations to handle large amounts of information. These frameworks often provide features for scheduling, monitoring, and error handling…
Model Retraining Frameworks
Model retraining frameworks are systems or tools designed to automate and manage the process of updating machine learning models with new data. These frameworks help ensure that models stay accurate and relevant as information and patterns change over time. By handling data collection, training, validation, and deployment, they make it easier for organisations to maintain…
Model Inference Optimization
Model inference optimisation is the process of making machine learning models run faster and more efficiently when they are used to make predictions. This involves improving the way models use computer resources, such as memory and processing power, without changing the results they produce. Techniques may include simplifying the model, using better hardware, or modifying…
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…
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…
Neural Inference Optimization
Neural inference optimisation refers to improving the speed and efficiency of running trained neural network models, especially when making predictions or classifications. This process involves adjusting model structures, reducing computational needs, and making better use of hardware to ensure faster results. It is especially important for deploying AI on devices with limited resources, such as…
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…