Output tracing is the process of following the results or outputs of a system, program, or process to understand how they were produced. It helps track the flow of information from input to output, making it easier to diagnose errors and understand system behaviour. By examining each step that leads to a final output, output…
Query replay is a process used in databases and software systems to run previously recorded queries again, usually in a test or development environment. It helps teams understand how changes to a system might affect performance, stability, or correctness by simulating real user activity. This technique is often used before deploying updates to ensure that…
Model chaining is a technique in artificial intelligence where the output from one model is used as the input for another model. This allows multiple models to work together to solve complex tasks that a single model might not handle well alone. By passing information through a sequence of models, each step can add value…
Script flattening is the process of combining multiple code files or modules into a single script. This is often done to simplify deployment, improve loading times, or make it harder to reverse-engineer code. By reducing the number of separate files, script flattening can help manage dependencies and ensure that all necessary code is included together.
Response caching is a technique used in web development to store copies of responses to requests, so that future requests for the same information can be served more quickly. By keeping a saved version of a response, servers can avoid doing the same work repeatedly, which saves time and resources. This is especially useful for…
Model retraining systems are automated frameworks or processes that update machine learning models with new data over time. These systems help keep models accurate and relevant as patterns and information change. By retraining models regularly, organisations ensure that predictions and decisions based on these models remain reliable and effective.
Model inference frameworks are software tools or libraries that help run machine learning models to make predictions or decisions using new data. They focus on efficiently using trained models, often optimising for speed, memory usage, and hardware compatibility. These frameworks support deploying models on various devices, such as servers, mobile phones, or embedded systems.
Data pipeline frameworks are software tools or platforms used to move, process, and manage data from one place to another. They help automate the steps required to collect data, clean it, transform it, and store it in a format suitable for analysis or further use. These frameworks make it easier and more reliable to handle…
Cloud-native frameworks are sets of tools and libraries designed to help developers build and run applications that fully use the benefits of cloud computing. These frameworks support features like automatic scaling, resilience, and easy updates, making it simpler to manage complex software. They often encourage breaking software into small, manageable parts that can be deployed…
Model inference systems are software tools or platforms that use trained machine learning models to make predictions or decisions based on new data. They take a model that has already learned from historical information and apply it to real-world inputs, producing useful outputs such as answers, classifications, or recommendations. These systems are often used in…