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…
Category: MLOps & Deployment
Model Compression Pipelines
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
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
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
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
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 Optimization
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.
Model Serving Architectures
Model serving architectures are systems designed to make machine learning models available for use after they have been trained. These architectures handle tasks such as receiving data, processing it through the model, and returning results to users or applications. They can range from simple setups on a single computer to complex distributed systems that support…
Continuous Model Training
Continuous model training is a process in which a machine learning model is regularly updated with new data to improve its performance over time. Instead of training a model once and leaving it unchanged, the model is retrained as fresh information becomes available. This helps the model stay relevant and accurate, especially when the data…
Model Versioning Systems
Model versioning systems are tools and methods used to keep track of different versions of machine learning models as they are developed and improved. They help teams manage changes, compare performance, and ensure that everyone is working with the correct model version. These systems store information about each model version, such as training data, code,…