Lead scoring is a method used by businesses to rank potential customers based on how likely they are to buy a product or service. This process assigns points to leads depending on their behaviour, such as visiting a website, opening emails, or filling in forms. The goal is to help sales and marketing teams focus…
An Applicant Tracking System (ATS) is software used by organisations to manage the recruitment process. It helps collect, sort, and track job applications and candidates throughout the hiring stages. ATS platforms automate tasks such as posting jobs, screening CVs, and scheduling interviews, making it easier for recruiters to organise and find the best candidates.
Recruitment software is a digital tool that helps organisations manage the process of finding and hiring new employees. It typically automates tasks such as posting job adverts, sorting CVs, communicating with candidates, and scheduling interviews. By streamlining these steps, recruitment software saves time, reduces manual errors, and improves the overall hiring process.
Appointment scheduling is the process of organising and managing times for meetings, services, or events between people or groups. It often involves selecting a suitable date and time, confirming availability, and sending reminders. This can be done manually using paper diaries or digitally through software and online tools.
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.
Feature importance analysis is a technique used in data science and machine learning to determine which input variables, or features, have the most influence on the predictions of a model. By identifying the most significant features, analysts can better understand how a model makes decisions and potentially improve its performance. This process also helps to…
Deep generative models are a type of artificial intelligence that can learn to create new data similar to the data they have been trained on. They use deep neural networks to understand patterns and structures in data such as images, text, or sound. Once trained, these models can generate new content that looks or sounds…
Convolutional neural filters are small sets of weights used in convolutional neural networks to scan input data, such as images, and detect patterns like edges or textures. They move across the input in a sliding window fashion, producing feature maps that highlight specific visual features. By stacking multiple filters and layers, the network can learn…
Deep Residual Learning is a technique used to train very deep neural networks by allowing the model to learn the difference between the input and the output, rather than the full transformation. This is done by adding shortcut connections that skip one or more layers, making it easier for the network to learn and avoid…