Data synchronisation pipelines are systems or processes that keep information consistent and up to date across different databases, applications, or storage locations. They move, transform, and update data so that changes made in one place are reflected elsewhere. These pipelines often include steps to check for errors, handle conflicts, and make sure data stays accurate…
Category: MLOps & Deployment
Secure Deployment Pipelines
A secure deployment pipeline is a series of automated steps that safely moves software changes from development to production. It includes checks and controls to make sure only approved, tested, and safe code is released. Security measures like code scanning, access controls, and audit logs are built into the process to prevent mistakes or malicious…
DevSecOps Automation
DevSecOps automation is the practice of integrating security checks and processes directly into the automated workflows of software development and IT operations. Instead of treating security as a separate phase, it becomes a continuous part of building, testing, and deploying software. This approach helps teams find and fix security issues early, reducing risks and improving…
Secure Model Sharing
Secure model sharing is the process of distributing machine learning or artificial intelligence models in a way that protects the model from theft, misuse, or unauthorised access. It involves using methods such as encryption, access controls, and licensing to ensure that only approved users can use or modify the model. This is important for organisations…
Inference Optimization Techniques
Inference optimisation techniques are methods used to make machine learning models run faster and use less computer power when making predictions. These techniques focus on improving the speed and efficiency of models after they have already been trained. Common strategies include reducing the size of the model, simplifying its calculations, or using special hardware to…
Robust Training Pipelines
Robust training pipelines are systematic processes for building, testing and deploying machine learning models that are reliable and repeatable. They handle tasks like data collection, cleaning, model training, evaluation and deployment in a way that minimises errors and ensures consistency. By automating steps and including checks for data quality or unexpected issues, robust pipelines help…
Anomaly Detection Pipelines
Anomaly detection pipelines are automated processes that identify unusual patterns or behaviours in data. They work by collecting data, cleaning it, applying algorithms to find outliers, and then flagging anything unexpected. These pipelines help organisations quickly spot issues or risks that might not be visible through regular monitoring.
Dynamic Inference Scheduling
Dynamic inference scheduling is a technique used in artificial intelligence and machine learning systems to decide when and how to run model predictions, based on changing conditions or resource availability. Instead of running all predictions at fixed times or in a set order, the system adapts its schedule to optimise performance, reduce delays, or save…
Secure AI Model Deployment
Secure AI model deployment is the process of making artificial intelligence models available for use while ensuring they are protected from cyber threats and misuse. It involves safeguarding the model, the data it uses, and the systems that run it. This helps maintain privacy, trust, and reliability when AI solutions are put into operation.
Data Pipeline Automation
Data pipeline automation is the process of automatically moving, transforming and managing data from one place to another without manual intervention. It uses tools and scripts to schedule and execute steps like data collection, cleaning and loading into databases or analytics platforms. This helps organisations process large volumes of data efficiently and reliably, reducing human…