Category: AI Infrastructure

AI Middleware Design Patterns

AI middleware design patterns are reusable solutions for connecting artificial intelligence components with other parts of a software system. These patterns help manage the flow of data, communication, and processing between AI services and applications. They simplify the integration of AI features by providing standard ways to handle tasks like request routing, data transformation, and…

Multi-Model Version Control

Multi-Model Version Control is a system for tracking and managing changes to multiple machine learning or AI models over time. It helps teams keep organised records of different model versions, including updates, experiments, and configurations. This process makes it easier to collaborate, roll back changes, and ensure reproducibility when working with several models at once.

Knowledge Injection Pipelines

Knowledge injection pipelines are automated processes that add up-to-date or specialised information into machine learning models or artificial intelligence systems. These pipelines gather data from trusted sources, clean and organise it, then integrate it so the AI can use the new knowledge effectively. This approach helps systems stay accurate and relevant without needing to be…

Query Cost Predictors

Query cost predictors are tools or algorithms that estimate how much computer resources, such as time and memory, a database query will use before it is run. These predictions help database systems choose the most efficient way to process and return the requested information. Accurate query cost prediction can improve performance and reduce waiting times…

LLM App Latency Diagnostics

LLM App Latency Diagnostics refers to the process of identifying, measuring and analysing delays that occur when a large language model (LLM) application responds to user requests. It involves tracking the time taken at each step, from receiving the query to delivering the final answer, to find slow points in the system. By understanding where…

Cross-Model Memory Sharing

Cross-Model Memory Sharing is a technique that allows different machine learning models or artificial intelligence systems to access and use the same memory or data storage. This means that information learned or stored by one model can be directly used by another without duplication. It helps models work together more efficiently, saving resources and improving…