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…
Category: MLOps & Deployment
Data Pipeline Automation
Data pipeline automation refers to the process of setting up systems that automatically collect, process, and move data from one place to another without manual intervention. These automated pipelines ensure data flows smoothly between sources, such as databases or cloud storage, and destinations like analytics tools or dashboards. By automating data movement and transformation, organisations…
Cloud Resource Monitoring
Cloud resource monitoring is the process of keeping track of how different resources such as servers, databases, and storage are used within a cloud computing environment. It involves collecting data on performance, availability, and usage to ensure that everything is running smoothly. By monitoring these resources, organisations can detect problems early, optimise costs, and maintain…
Model Retraining Pipelines
Model retraining pipelines are automated processes that regularly update machine learning models using new data. These pipelines help ensure that models stay accurate and relevant as conditions change. By automating the steps of collecting data, processing it, training the model, and deploying updates, organisations can keep their AI systems performing well over time.
Model Performance Tracking
Model performance tracking is the process of monitoring how well a machine learning or statistical model is working over time. It involves collecting and analysing data about the model’s predictions compared to real outcomes. This helps teams understand if the model is accurate, needs updates, or is drifting from its original performance.
Data Pipeline Monitoring
Data pipeline monitoring is the process of tracking the movement and transformation of data as it flows through different stages of a data pipeline. It helps ensure that data is being processed correctly, without errors or unexpected delays. Monitoring tools can alert teams to problems, such as failed data transfers or unusual patterns, so they…
Model Deployment Automation
Model deployment automation is the process of automatically transferring machine learning models from development to a live environment where they can be used by others. It involves using tools and scripts to handle steps like packaging the model, testing it, and setting it up on servers without manual work. This makes it easier, faster, and…
Model Inference Scaling
Model inference scaling refers to the process of increasing a machine learning model’s ability to handle more requests or data during its prediction phase. This involves optimising how a model runs so it can serve more users at the same time or respond faster. It often requires adjusting hardware, software, or system architecture to meet…
Inference Acceleration Techniques
Inference acceleration techniques are methods used to make machine learning models, especially those used for predictions or classifications, run faster and more efficiently. These techniques reduce the time and computing power needed for a model to process new data and produce results. Common approaches include optimising software, using specialised hardware, and simplifying the model itself.
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.