Model retraining pipelines are automated systems that regularly update machine learning models with new data. They help ensure that models stay accurate and relevant as real-world conditions change. These pipelines handle tasks such as collecting fresh data, retraining the model, validating its performance, and deploying the updated version.
Model Lifecycle Management is the process of overseeing machine learning or artificial intelligence models from their initial creation through deployment, ongoing monitoring, and eventual retirement. It ensures that models remain accurate, reliable, and relevant as data and business needs change. The process includes stages such as development, testing, deployment, monitoring, updating, and decommissioning.
Model deployment automation is the process of using tools and scripts to automatically move machine learning models from development to a production environment. This reduces manual work, speeds up updates, and helps ensure that models are always running the latest code. Automated deployment can also help catch errors early and maintain consistent quality across different…
Data pipeline monitoring is the process of tracking and observing the flow of data through automated systems that move, transform, and store information. It helps teams ensure that data is processed correctly, on time, and without errors. By monitoring these pipelines, organisations can quickly detect issues, prevent data loss, and maintain the reliability of their…
Model compression pipelines are a series of steps used to make machine learning models smaller and faster without losing much accuracy. These steps can include removing unnecessary parts of the model, reducing the precision of calculations, or combining similar parts. The goal is to make models easier to use on devices with limited memory or…
Efficient model inference refers to the process of running machine learning models in a way that minimises resource use, such as time, memory, or computing power, while still producing accurate results. This is important for making predictions quickly, especially on devices with limited resources like smartphones or embedded systems. Techniques for efficient inference can include…
Automation scalability frameworks are structured methods or tools designed to help automation systems handle increased workloads or more complex tasks without losing performance or reliability. They provide guidelines, software libraries, or platforms that make it easier to expand automation across more machines, users, or processes. By using these frameworks, organisations can grow their automated operations…
Microservices deployment models describe the different ways independent software components, called microservices, are set up and run in computing environments. These models help teams decide how to package, deploy and manage each service so they work together smoothly. Common models include deploying each microservice in its own container, running multiple microservices in the same container…
Model performance tracking is the process of monitoring how well a machine learning model is working over time. It involves collecting and analysing data on the model’s predictions to see if it is still accurate and reliable. This helps teams spot problems early and make improvements when needed.
Inference pipeline optimisation is the process of making the steps that turn machine learning models into predictions faster and more efficient. It involves improving how data is prepared, how models are run, and how results are delivered. The goal is to reduce waiting time and resource usage while keeping results accurate and reliable.